Evaluating Cell Processes, Quality, and Biomarkers in Pluripotent Stem Cells Using Video Bioinformatics

Abstract

There is a foundational need for quality control tools in stem cell laboratories engaged in basic research, regenerative therapies, and toxicological studies. These tools require automated methods for evaluating cell processes and quality during in vitro passaging, expansion, maintenance, and differentiation. In this paper, an unbiased, automated high-content profiling toolkit, StemCellQC, is presented that non-invasively extracts information on cell quality and cellular processes from time-lapse phase-contrast videos. Twenty four (24) morphological and dynamic features were analyzed in healthy, unhealthy, and dying human embryonic stem cell (hESC) colonies to identify those features that were affected in each group. Multiple features differed in the healthy versus unhealthy/dying groups, and these features were linked to growth, motility, and death. Biomarkers were discovered that predicted cell processes before they were detectable by manual observation. StemCellQC distinguished healthy and unhealthy/dying hESC colonies with 96% accuracy by non-invasively measuring and tracking dynamic and morphological features over 48 hours. Changes in cellular processes can be monitored by StemCellQC and predictions can be made about the quality of pluripotent stem cell colonies. This toolkit reduced the time and resources required to track multiple pluripotent stem cell colonies and eliminated handling errors and false classifications due to human bias. StemCellQC provided both user-specified and classifier-determined analysis in cases where the affected features are not intuitive or anticipated. Video analysis algorithms allowed assessment of biological phenomena using automatic detection analysis, which can aid facilities where maintaining stem cell quality and/or monitoring changes in cellular processes are essential. In the future StemCellQC can be expanded to include other features, cell types, treatments, and differentiating cells.

Distribution, quantification and toxicity of cinnamaldehyde in electronic cigarette refill fluids and aerosols

“…The reduced colony growth in the cinnamaldehyde group may have been due to decreased proliferation, increased cell death (supported by the brightness/area ratio data) or a combination of these factors. The greater motility in the cinnamaldehyde-treated group likely occurs because the larger control colonies required more energy and coordination for directed movement, and/or because the exposed colonies attempt to evade cinnamaldehyde exposure 26. Similar responses in growth, motility and apoptosis occurred in hESCs treated with sidestream cigarette smoke 26…”

“…Time-lapse phase contrast images of control and treated cells were taken every hour for 70 hours in a Nikon BioStation CT using 3×3 tiling. Videos were generated and analysed using StemCellQC video bioinformatics software 26. Totally, 10–15 colonies/group were analysed in three independent experiments.…”

Thirdhand Smoke: New Evidence, Challenges, and Future Directions

“…Assays can be acute or chronic, end points can be single or multiplexed, and designs can be low- or high-throughput. 76 Stem cells and differentiated cells from any organ can be compared, and THS can be studied independently from SHS. In vitro work can either establish a foundation upon which in vivo work with animals and humans can be based or it can provide a means to study results obtained in vivo in more depth.…”

Optical-flow based non-invasive analysis of cardiomyocyte contractility

“… 31 established, optical-flow based techniques offer a useful estimate for the temporal dynamics of cardiomyocyte contractility in vitro . Accordingly, optical-flow has been used in several recent studies to characterize contractile cycles 6 , 9 – 11 , 32 . Here we demonstrate that by additional image processing steps, from the optical flow data one can obtain a both biologically and mechanically relevant spatial map of cell contractility.…”

“…Thus, quantifying contractility is essential for measuring the functionality of cardiomyocytes. Unlike most current technologies, optical flow-based (particle image velocimetry, PIV) methods 6 , 9 – 11 are capable of monitoring cardiomyocyte contractile function without physical or biochemical manipulations, hence without compromising cell quality.…”