Basic and Preclinical Research for Personalized Medicine

Stable Housekeeping Genes in Bone Marrow, Adipose Tissue, and Amniotic Membrane-Derived Mesenchymal Stromal Cells for Orthopedic Regenerative Medicine Approaches

The therapeutic effect of mesenchymal stromal cells (MSCs) has been described for a variety of disorders, including those affecting musculoskeletal tissues. In this context, the literature reports several data about the regenerative effectiveness of MSCs derived from bone marrow, adipose tissue, and an amniotic membrane (BMSCs, ASCs, and hAMSCs, respectively), either when expanded or when acting as clinical-grade biologic pillars of products used at the point of care. To date, there is no evidence about the superiority of one source over the others from a clinical perspective. Therefore, a reliable characterization of the tissue-specific MSC types is mandatory to identify the most effective treatment, especially when tailored to the target disease. Because molecular characterization is a crucial parameter for cell definition, the need for reliable normalizers as housekeeping genes (HKGs) is essential. In this report, the stability levels of five commonly used HKGs (ACTB, EF1A, GAPDH, RPLP0, and TBP) were sifted into BMSCs, ASCs, and hAMSCs. Adult and fetal/neonatal MSCs showed opposite HKG stability rankings. Moreover, by analyzing MSC types side-by-side, comparison-specific HKGs emerged. The effect of less performant HKG normalization was also demonstrated in genes coding for factors potentially involved in and predicting MSC therapeutic activity for osteoarthritis as a model musculoskeletal disorder, where the choice of the most appropriate normalizer had a higher impact on the donors rather than cell populations when compared side-by-side. In conclusion, this work confirms HKG source-specificity for MSCs and suggests the need for cell-type specific normalizers for cell source or condition-tailored gene expression studies.

Mesenchymal stem cells and their extracellular vesicles in urological cancers: Prostate, bladder, and kidney

Mesenchymal stem cells (MSCs) are recognized for their remarkable ability to differentiate into multiple cell types. They are also known to possess properties that can fight cancer, leading to attempts to modify MSCs for use in anticancer treatments. However, MSCs have also been found to participate in pathways that promote tumor growth. Many studies have been conducted to explore the potential of MSCs for clinical applications, but the results have been inconclusive, possibly due to the diverse nature of MSC populations. Furthermore, the conflicting roles of MSCs in inhibiting tumors and promoting tumor growth hinder their adaptation to anticancer therapies. Antitumorigenic and protumorigenic properties of MSCs in urological cancers such as bladder, prostate, and renal are not as well established, and data comparing them are still limited. MSCs hold significant promise as a vehicle for delivering anticancer agents and suicide genes to tumors. Presently, numerous studies have concentrated on the products derived from MSCs, such as extracellular vesicles (EVs), as a form of cell-free therapy. This work aimed to review and discuss the current knowledge of MSCs and their EVs in urological cancer therapy.

Cluster Donation: How Future Healthcare Professionals Bound Certain Types of Tissues and Biomedical Research and How It Affects Their Willingness to Donate

Although biomedical research requires cooperation with a large number of donors, its success also depends on the input of healthcare professionals who play a crucial role in promoting biomedical research and influencing an individual's decision to donate one's biospecimens that are left over after a medical procedure. This work was aimed at investigating the correlation between medical and healthcare students' willingness to donate a biospecimen, the type of tissues to be donated, and the type of biomedical research to be conducted. A population survey among medical and healthcare students enrolled at the Poznan University of Medical Sciences was conducted on their attitudes toward the donation of human biological material for research purposes. A total of 1500 students responded and completed the survey. The questionnaire used multiple-choice closed-ended questions designed to explore medical and healthcare students' preferences for sharing particular types of tissues and donating to a particular type of biomedical research. It asked three questions: 1. Which type of tissue would people be willing to donate for research purposes? 2. Which organs would they be willing to donate after death? 3. What type of research would they be willing to donate to? While future healthcare professionals' beliefs regarding certain types of tissues and research can influence their willingness to donate for research purposes, many students tend to think about the body and biomedical research in terms of clusters. Consequently, their willingness to donate certain tissues for a particular type of research can affect their decision to donate other tissues. Our data suggest that cluster thinking with regard to donation can be a predictor of people's readiness to participate in the collection and management of biospecimens.

Structured tracking of alcohol reinforcement (STAR) for basic and translational alcohol research

There is inherent tension between methodologies developed to address basic research questions in model species and those intended for preclinical to clinical translation: basic investigations require flexibility of experimental design as hypotheses are rapidly tested and revised, whereas preclinical models emphasize standardized protocols and specific outcome measures. This dichotomy is particularly relevant in alcohol research, which spans a diverse range of basic sciences in addition to intensive efforts towards understanding the pathophysiology of alcohol use disorder (AUD). To advance these goals there is a great need for approaches that facilitate synergy across basic and translational areas of nonhuman alcohol research. In male and female mice, we establish a modular alcohol reinforcement paradigm: Structured Tracking of Alcohol Reinforcement (STAR). STAR provides a robust platform for quantitative assessment of AUD-relevant behavioral domains within a flexible framework that allows direct crosstalk between translational and mechanistically oriented studies. To achieve cross-study integration, despite disparate task parameters, a straightforward multivariate phenotyping analysis is used to classify subjects based on propensity for heightened alcohol consumption and insensitivity to punishment. Combining STAR with extant preclinical alcohol models, we delineate longitudinal phenotype dynamics and reveal putative neuro-biomarkers of heightened alcohol use vulnerability via neurochemical profiling of cortical and brainstem tissues. Together, STAR allows quantification of time-resolved biobehavioral processes essential for basic research questions simultaneous with longitudinal phenotyping of clinically relevant outcomes, thereby providing a framework to facilitate cohesion and translation in alcohol research.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.