Connected components were then found in the binary image. using ImageJ. 2-way ANOVA exposed no significant variations. (C, D) Normalized area and perimeter ideals for unhealthy colonies extracted by StemCellQC compared to floor truth using ImageJ. 2-way ANOVA exposed no significant variations. (E, F) Normalized area and perimeter ideals for dying colonies extracted by StemCellQC compared to floor truth using ImageJ. 2-way ANOVA exposed no significant variations, except for a portion of the normalized part of dying colonies. This corresponds with minor over-segmentation of software due to detection of cellular debris ejected from dying SR9238 colonies after their death at 30hours (* = P 0.05).(TIF) pone.0148642.s003.tif (2.9M) GUID:?AEFD523A-C286-476F-A6D4-5368F3074C83 S4 Fig: Relationship Cd8a between features and cell processes. (TIF) pone.0148642.s004.tif (1.0M) GUID:?1A0DC7B5-7560-4DAB-AA38-67D7B2BC5FDE S5 Fig: Visual descriptors of extracted features related to area. (TIF) pone.0148642.s005.tif (4.3M) GUID:?3EC82BDD-BAF7-4E23-85FF-01D52FDA328A S6 Fig: Visual descriptors of extracted features related to morphology and area. (TIF) pone.0148642.s006.tif (7.3M) GUID:?8BB06B10-53DE-495C-8AD2-DC098AA59641 S7 Fig: Visual descriptors of extracted features related to motility. (TIF) pone.0148642.s007.tif (2.9M) GUID:?A6EDD801-1EF5-4A61-8053-2651B66BC165 S8 Fig: Visual descriptors of extracted features related to apoptosis. (TIF) pone.0148642.s008.tif (5.2M) GUID:?2707B547-35CF-4777-8492-B4E35C05409E S9 Fig: List of Extracted Features and Definitions. (TIF) pone.0148642.s009.tif (1.2M) GUID:?93373F11-0BD4-4407-8649-8F4502259CB2 S1 Video: Average intensity versus perimeter operating plot shown for those individual healthy (green), unhealthy (blue), and dying (reddish) hESC colonies. (MPG) pone.0148642.s010.mpg (1.8M) GUID:?ACC8875F-E2BE-4BE3-8664-953478A946A2 S2 Video: Mean-squared displacement versus area operating plot shown for those individual healthy (green), unhealthy (blue), and dying (reddish) hESC colonies. (MPG) pone.0148642.s011.mpg (1.3M) GUID:?D1409923-54F5-4273-AB53-5B20F489EE10 S3 Video: Phase contrast video of a representative healthy colony with the segmentation layed out in white. (MPG) pone.0148642.s012.mpg (4.9M) GUID:?42617B07-2E39-4372-82C9-C30F57DDE693 S4 Video: Protrusions feature video of a representative healthy colony with the protrusions layed out in reddish. (MPG) pone.0148642.s013.mpg (7.0M) GUID:?6D286A6C-2FF0-42E9-8653-AEC8CDC3C589 S5 Video: Bright-to-total area ratio feature video with SR9238 the bright dead cells of a representative unhealthy colony highlighted in white. (MPG) pone.0148642.s014.mpg (3.9M) GUID:?039FBF4B-ACF7-4891-BC69-2A9C0606C3C4 S6 Video: Solidity feature video of a representative dying colony with the convex hull shown in white and the colony segmentation outlined in red. (MPG) pone.0148642.s015.mpg (4.1M) GUID:?DD2DA0EF-BE51-49DD-9DE8-D52A37B7F080 Data Availability StatementAll relevant data are within the paper and its Supporting Information documents. 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 passaging, growth, maintenance, and differentiation. With this paper, an unbiased, automated high-content profiling toolkit, StemCellQC, is definitely offered that non-invasively components info on cell quality and cellular processes from time-lapse phase-contrast video clips. Twenty four (24) morphological and dynamic features were analyzed in healthy, unhealthy, and dying human being embryonic stem cell (hESC) colonies to identify SR9238 those features that were affected in each group. Multiple features differed in the healthy versus unhealthy/dying organizations, and these features were linked to growth, motility, and death. Biomarkers were discovered that expected 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 being bias. StemCellQC offered 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 keeping stem cell quality and/or monitoring changes in cellular processes are essential. In the future StemCellQC can be expanded to include additional features, cell types, treatments, and differentiating cells. Intro SR9238 Human being pluripotent stem cells (hPSC) have enormous potential for enhancing our understanding of human being prenatal development, modeling diseases-in-a-dish, treating individuals with degenerative diseases, and evaluating the effects of medicines and environmental chemicals on cells that model human being embryos and fetuses.
