The proteins secreted by human tissues (the secretome) are important for the basic understanding of human biology, but also for identification of potential targets for future diagnosis and therapy. Here, we present an annotation of all predicted secreted proteins (n=2,623) with information about their spatial distribution in the human body. A high-throughput mammalian cell factory was established to create a resource of recombinant full-length proteins. This resource was used for phenotypic assays involving β-cell dedifferentiation and for development of targeted proteomics assays. A comparison between host cells, including omics analysis, shows that many of the proteins that failed to be generated in CHO cells could be rescued in human HEK293 cells. In conclusion, the human secretome has been mapped and characterized to facilitate further exploration of the human secretome.

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Identification of small molecules with the potential to selectively proliferate cardiac progenitor cells (CPCs) will aid our understanding of the signaling pathways and mechanisms involved and could ultimately provide tools for regenerative therapies for the treatment of post-MI cardiac dysfunction. We have used an in vitro human induced pluripotent stem cellderived CPC model to screen a 10,000-compound library containing molecules representing different target classes and compounds reported to modulate the phenotype of stem or primary cells. The primary readout of this phenotypic screen was proliferation as measured by nuclear count. We identified retinoic acid receptor (RAR) agonists as potent proliferators of CPCs. The CPCs retained their progenitor phenotype following proliferation and the identified RAR agonists did not proliferate human cardiac fibroblasts, the major cell type in the heart. In addition, the RAR agonists were able to proliferate an independent source of CPCs, HuES6. The RAR agonists had a time-of-differentiation-dependent effect on the HuES6-derived CPCs. At 4 days of differentiation, treatment with retinoic acid induced differentiation of the CPCs to atrial cells. However, after 5 days of differentiation treatment with RAR agonists led to an inhibition of terminal differentiation to cardiomyocytes and enhanced the proliferation of the cells. RAR agonists, at least transiently, enhance the proliferation of human CPCs, at the expense of terminal cardiac differentiation. How this mechanism translates in vivo to activate endogenous CPCs and whether enhancing proliferation of these rare progenitor cells is sufficient to enhance cardiac repair remains to be investigated.

Cell-based assays have long been important within hit discovery paradigms; however, improving the disease relevance of the assay system can positively affect the translation of small-molecule drug discovery, especially if adopted in the initial hit identification assay. Consequently, there is an increasing need for disease-relevant assay systems capable of running at large scale, including the use of induced pluripotent stem cells and donor-derived primary cells. Major hurdles to adopting these assays for high-throughput screening are the cost, availability of cells, and complex protocols. Miniaturization of such assays to 1536-well format is an approach that can reduce costs and increase throughput. Adaptation of these complex cell assays to 1536-well format brings major challenges in liquid handling for high-content assays requiring washing steps and coating of plates. In addition, problematic edge effects and reduced assay quality are frequently encountered. In this study, we describe the novel application of a centrifugal plate washer to facilitate miniaturization of a range of 1536-well cell assays and techniques to reduce edge effects, all of which improved throughput and data quality. Cell assays currently limited in throughput because of cost and complex protocols may be enabled by the techniques presented in this study.

While deep learning has seen many recent applications to drug discovery, most have focused on predicting activity or toxicity directly from chemical structure. Phenotypic changes exhibited in cellular images are also indications of the mechanism of action (MoA) of chemical compounds. In this paper, we show how pre-trained convolutional image features can be used to assist scientists in discovering interesting chemical clusters for further investigation. Our method reduces the dimensionality of raw fluorescent stained images from a high throughput imaging (HTI) screen, producing an embedding space that groups together images with similar cellular phenotypes. Running standard unsupervised clustering on this embedding space yields a set of distinct phenotypic clusters. This allows scientists to further select and focus on interesting clusters for downstream analyses. We validate the consistency of our embedding space qualitatively with t-sne visualizations, and quantitatively by measuring embedding variance among images that are known to be similar. Results suggested the usefulness of our proposed workflow using deep learning and clustering and it can lead to robust HTI screening and compound triage.