FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines.

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array®.

Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.

Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson's disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes.

Osteoblastic MG-63 cell differentiation, contraction, and mRNA expression in stress-relaxed 3D collagen I gels.

To investigate the molecular aspects of osteoblastic interactions with a type I collagen matrix, human osteoblast-like MG-63 cells were cultured in three-dimensional (3D) collagen I gels. MG-63 cells in collagen gels expressed higher osteocalcin mRNA levels than cells in monolayer (2D) on polystyrene surfaces. Gel contraction was assessed via releasing the collagen gels from attachment following 24 h incubation in serum free, TGF-beta1-treated, or 1,25-(OH)(2)D(3)-treated media.