 Welcome to the video abstract of our recent paper entitled the model-based cell number quantification using online single oxygen sensor data for tissue engineering perfusion biorectors. We would first like to explain you about the need for cost-effective monitoring tools in biorectors and then we would like to introduce you to our newly developed method for the non-invasive and online quantification of total cell number in a biorector. Robust automation of the cell culture process requires monitoring and control of the critical culture parameters. However, current biorectors are still very black box and have limited monitoring capabilities. Although multiple sensors are available that allow us to get information from the micro environment inside the biorector, there are still limited monitoring tools that provide us data on the tissue construct quality such as cell number or metabolic activity. In this work, therefore, we developed a monitoring tool that allows us to quantify a large range of cell numbers inside the biorectors by combining a data-based modeling approach on data from a single oxygen sensor and step changes in the perfusion flow rate. 21 scaffold combination with different total cell numbers were perfused in the biorector system. The oxygen concentration at the inlet of the perfusion chamber is kept constant with the aid of an oxygenator and the oxygen concentration at the outlet of the perfusion chamber is registered with an oxygen sensor. Note that there is only one sensor used for this approach. The aim is not to use the difference in oxygen concentration over the perfusion chamber as a measure for cell number, but instead we make use of a dynamic flow rate. By applying step changes in flow rate, the dissolved oxygen concentration per unit volume of medium is altered due to cellular consumption proportional to the residence time of the medium in the scaffold. The resulting dynamic oxygen response to the perfusion rate can be measured by a single sensor at the perfusion chamber outlet. This dynamic oxygen response could be modeled with a data-based model of which the steady-state gain parameter can be directly correlated to the number of cells present in the perfusion chamber. The dynamics described by the steady-state gain parameter of the data-based model was also interpreted in a mechanistic sense with a model that links a drop in oxygen due to the step change in flow rate with the number of cells in the scaffolds. So in conclusion, the data-based mechanistic modeling approach combined with step changes in the perfusion flow rate allows for an accurate quantification of a broad range of cell numbers within a perfusion biorector. And this in an online and a non-destructive way. By using only one oxygen sensor, this approach both combines accuracy and cost-effectiveness. In addition to the non-invasiveness and the relative simplicity of this setup, the approach used here is generically applicable in different culture applications, for example in different biorector types or with different cell types. And this makes it an interesting tool for quality control. As the field of tissue engineering matures and the transition from bench scale to large-scale industrialized production takes place, a new set of biological and technological challenges arises. The steady increase of early and late-stage clinical trials involving cell therapy applications as well as the presence of approved pioneer commercial cell-based products in the market strongly indicate that the cell therapy industry is on its way to evolve into a novel healthcare sector. To this end, regulatory bodies such as the EMA and FDA are raising the importance of enhanced process understanding through improved monitoring, being key to achieving high-quality tissue engineered end-products. According to the bioreactor system of choice, different strategies may be followed to extract the maximum amount of information non-invasively, taking also into account economic feasibility. To be able to achieve this, it will be necessary to develop online tools and data-based modeling approaches following the so-called quality-by-design approach that will enable meaningful online monitoring of critical quality attributes over time.