Teachable, high-content analytics for live-cell, phase contrast movies

Reduced Cell Migration in Human Chondrocyte Sheets Increases Tissue Stiffness and Cartilage Protein Production

BACKGROUND

Chondrogenic differentiation medium (CDM) is usually used to maintain chondrogenic activity during chondrocyte sheet production. However, tissue qualities remain to be determined as to what factors improve cell functions. Moreover, the relationship between CDM and cell migration proteins has not been reported.

METHOD

In this study, the effect of CDM on the behavior of chondrocyte sheets was investigated. Structural analysis, mechanical testing and proteomics were performed to observe tissue qualities. The relationship between CDM and cell migration proteins were investigated using time-lapse observations and bioinformatic analysis.

RESULTS

During 48 h, CDM affected the chondrocyte behaviors by reducing cell migration. Compared to the basal medium, CDM impacted the contraction of monolayered chondrocyte sheets. At day 7, the contracted sheets increased tissue thickness and improved tissue stiffness. Cartilage specific proteins were also upregulated. Remarkedly, the chondrocyte sheets in CDM displayed downregulated proteins related to cell migration. Bioinformatic analysis revealed that TGFβ1 was shown to be associated with cartilage functions and cell migration. Pathway analysis of chondrocyte sheets in CDM also revealed the presence of a TGFβ pathway without activating actin production, which might be involved in synthesizing cartilage-specific proteins. Cell migration pathway showed MAPK signaling in both cultures of the chondrocyte sheets.

CONCLUSION

Reduced cell migration in the chondrocyte sheet affected the tissue quality. Using CDM, TGFβ1 might trigger cartilage protein production through the TGFβ pathway and be involved in cell migration via the MAPK signaling pathway. Understanding cell behaviors and their protein expression would be beneficial for developing high-quality tissue-engineered cartilage.

Automated algorithm development to assess survival of human neurons using longitudinal single-cell tracking: Application to synucleinopathy

The development of phenotypic assays with appropriate analyses is an important step in the drug discovery process. Assays using induced pluripotent stem cell (iPSC)-derived human neurons are emerging as powerful tools for drug discovery in neurological disease. We have previously shown that longitudinal single cell tracking enabled the quantification of survival and death of neurons after overexpression of α-synuclein with a familial Parkinson's disease mutation (A53T). The reliance of this method on manual counting, however, rendered the process labor intensive, time consuming and error prone. To overcome these hurdles, we have developed automated detection algorithms for neurons using the BioStation CT live imaging system and CL-Quant software. In the current study, we use these algorithms to successfully measure the risk of neuronal death caused by overexpression of α-synuclein (A53T) with similar accuracy and improved consistency as compared to manual counting. This novel method also provides additional key readouts of neuronal fitness including total neurite length and the number of neurite nodes projecting from the cell body. Finally, the algorithm reveals the neuroprotective effects of brain-derived neurotrophic factor (BDNF) treatment in neurons overexpressing α-synuclein (A53T). These data show that an automated algorithm improves the consistency and considerably shortens the analysis time of assessing neuronal health, making this method advantageous for small molecule screening for inhibitors of synucleinopathy and other neurodegenerative diseases.

Reproducible production and image-based quality evaluation of retinal pigment epithelium sheets from human induced pluripotent stem cells

Transplantation of retinal pigment epithelial (RPE) sheets derived from human induced pluripotent cells (hiPSC) is a promising cell therapy for RPE degeneration, such as in age-related macular degeneration. Current RPE replacement therapies, however, face major challenges. They require a tedious manual process of selecting differentiated RPE from hiPSC-derived cells, and despite wide variation in quality of RPE sheets, there exists no efficient process for distinguishing functional RPE sheets from those unsuitable for transplantation. To overcome these issues, we developed methods for the generation of RPE sheets from hiPSC, and image-based evaluation. We found that stepwise treatment with six signaling pathway inhibitors along with nicotinamide increased RPE differentiation efficiency (RPE6iN), enabling the RPE sheet generation at high purity without manual selection. Machine learning models were developed based on cellular morphological features of F-actin-labeled RPE images for predicting transepithelial electrical resistance values, an indicator of RPE sheet function. Our model was effective at identifying low-quality RPE sheets for elimination, even when using label-free images. The RPE6iN-based RPE sheet generation combined with the non-destructive image-based prediction offers a comprehensive new solution for the large-scale production of pure RPE sheets with lot-to-lot variations and should facilitate the further development of RPE replacement therapies.

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

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.

VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery

BACKGROUND

Existing de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise.

RESULTS

VISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20.

CONCLUSIONS

We demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Dynamic visualization of RANKL and Th17-mediated osteoclast function

Osteoclasts are bone resorbing, multinucleate cells that differentiate from mononuclear macrophage/monocyte-lineage hematopoietic precursor cells. Although previous studies have revealed important molecular signals, how the bone resorptive functions of such cells are controlled in vivo remains less well characterized. Here, we visualized fluorescently labeled mature osteoclasts in intact mouse bone tissues using intravital multiphoton microscopy. Within this mature population, we observed cells with distinct motility behaviors and function, with the relative proportion of static - bone resorptive (R) to moving - nonresorptive (N) varying in accordance with the pathophysiological conditions of the bone. We also found that rapid application of the osteoclast-activation factor RANKL converted many N osteoclasts to R, suggesting a novel point of action in RANKL-mediated control of mature osteoclast function. Furthermore, we showed that Th17 cells, a subset of RANKL-expressing CD4+ T cells, could induce rapid N-to-R conversion of mature osteoclasts via cell-cell contact. These findings provide new insights into the activities of mature osteoclasts in situ and identify actions of RANKL-expressing Th17 cells in inflammatory bone destruction.