The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Graphical Abstract Highlights d Implementing FAIR data standards requires identification of experimental confounders d Five labs performed the same experiment on mammalian cells and compared results d Several factors affecting reproducibility were explored d Biological context had an unexpected impact on the robustness of cell-based assays

Metformin treatment is associated with a decreased risk and better prognosis of pancreatic cancer (PC) in patients with type 2 diabetes, but the mechanism of metformin’s PC growth inhibition in the context of a prediabetic state is unknown. We used a Panc02 pancreatic tumor cell transplant model in diet-induced obese (DIO) C57BL/6 mice to compare the effects of metformin and the direct mammalian target of rapamycin (mTOR) inhibitor rapamycin on PC growth, glucose regulation, mTOR pathway signaling, and candidate microRNA (miR) expression. In DIO/prediabetic mice, metformin and rapamycin significantly reduced pancreatic tumor growth and mTOR-related signaling. The rapamycin effects centered on decreased mTOR-regulated growth and survival signaling, including increased expression of let-7b and cell cycle–regulating miRs. Metformin (but not rapamycin) reduced glucose and insulin levels and expression of miR-34a and its direct targets Notch, Slug, and Snail. Metformin also reduced the number and size of Panc02 tumor spheres in vitro and inhibited the expression of Notch in spheroids. Our results suggest that metformin and rapamycin can both inhibit pancreatic tumor growth in obese, prediabetic mice through shared and distinct mechanisms. Metformin and direct mTOR inhibitors, alone or possibly in combination, represent promising intervention strategies for breaking the diabetes-PC link.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.