In Vitro Research Reproducibility: Keeping Up High Standards

Abstract

Concern regarding the reproducibility of observations in life science research has emerged in recent years, particularly in view of unfavorable experiences with preclinical in vivo research. The use of cell-based systems has increasingly replaced in vivo research and the application of in vitro models enjoys an ever-growing popularity. To avoid repeating past mistakes, high standards of reproducibility and reliability must be established and maintained in the field of in vitro biomedical research. Detailed guidance documenting the appropriate handling of cells has been authored, but was received with quite disparate perception by different branches in biomedical research. In that regard, we intend to raise awareness of the reproducibility issue among scientists in all branches of contemporary life science research and their individual responsibility in this matter. We have herein compiled a selection of the most susceptible steps of everyday in vitro cell culture routines that have the potential to influence cell quality and recommend practices to minimize the likelihood of poor cell quality impairing reproducibility with modest investment of time and resources.

Insufficient transparency of statistical reporting in preclinical research: a scoping review

“…neuroscience, immunology, developmental science) in preclinical science. In particular, the inclusion of non-animal (cell) research in the present work is distinctive since reporting is often not presented as a component of reproducibility in research conducted in vitro 24 . Future investigation on the quality of reporting in research made in vitro might be useful.…”

3D Culture Systems for Exploring Cancer Immunology

“…3D culture systems provide a tremendous opportunity to explore cancer immunology, but they are not without limitations. The increased complexity of 3D systems can make inter- and intra-experiment reproducibility difficult [ 113 ]. 3D culture systems are more expensive and less widely available than 2D culture systems.…”

OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data

“…Reviewing the literature shows that reproducibility in the data and analysis is the subject of an extensive range of publications in different areas of science, e.g., life science, bioscience, medical and pharmaceutical science and translational science [2][3][4][5][6]. Studies have shown irreproducibility of results is often due to poor documentation of the statistical method [7][8][9]. This is especially critical for the high-throughput phenomic screening when tens of thousands of data points are generated and analysed.…”