Non-invasive real-time monitoring of glucose and lactate by NIR-spectroscopy during perfusion CHO culture

Near-infrared spectroscopy (NIR) has been used to monitor glycerol and methanol, non-invasively during cultures of Pichia pastoris1, 2. In this work, glucose and lactate were measured in Chinese Hamster Ovary (CHO) perfusion culture, in real time, using an online advanced NIR monitor and a reference offline biochemical analyzer. The 1.8-L culture started in the batch phase at 4 g/L glucose with 0.3 x 106 cells/mL and reached 1.5 x 106 cells/mL after 90 hours. Perfusion was then initiated and conducted for 10 days at 0.7 vvd, using a spiral membrane-less microfluidic device3. The maximum cell concentration was 4 x106 cells/mL at 160 hours and was maintained until the termination of the experiment. Online and offline trends were similar (Figure 1). Final concentrations of glucose and lactate were 2.2 g/L and 1.8 g/L, respectively. High performance liquid chromatography (HPLC) was used to measure IgG1

Reproducible image-based profiling with Pycytominer

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.Comment: 13 pages, 4 figure