CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

A fundamental problem in cancer drug development is that antitumor efficacy in preclinical cancer models does not translate faithfully to patient outcomes. Much of early cancer drug discovery is performed under in vitro conditions in cell-based models that poorly represent actual malignancies. To address this inconsistency, we have developed a technology platform called CIVO, which enables simultaneous assessment of up to eight drugs or drug combinations within a single solid tumor in vivo. The platform is currently designed for use in animal models of cancer and patients with superficial tumors but can be modified for investigation of deeper-seated malignancies. In xenograft lymphoma models, CIVO microinjection of well-characterized anticancer agents (vincristine, doxorubicin, mafosfamide, and prednisolone) induced spatially defined cellular changes around sites of drug exposure, specific to the known mechanisms of action of each drug. The observed localized responses predicted responses to systemically delivered drugs in animals. In pair-matched lymphoma models, CIVO correctly demonstrated tumor resistance to doxorubicin and vincristine and an unexpected enhanced sensitivity to mafosfamide in multidrug-resistant lymphomas compared with chemotherapy-naïve lymphomas. A CIVO-enabled in vivo screen of 97 approved oncology agents revealed a novel mTOR (mammalian target of rapamycin) pathway inhibitor that exhibits significantly increased tumor-killing activity in the drug-resistant setting compared with chemotherapy-naïve tumors. Finally, feasibility studies to assess the use of CIVO in human and canine patients demonstrated that microinjection of drugs is toxicity-sparing while inducing robust, easily tracked, drug-specific responses in autochthonous tumors, setting the stage for further application of this technology in clinical trials.

Lung cancer remains the most common cause of cancer-related mortality. We applied a highly multiplexed proteomic technology (SOMAscan) to compare protein expression signatures of non small-cell lung cancer (NSCLC) tissues with healthy adjacent and distant tissues from surgical resections. In this first report of SOMAscan applied to tissues, we highlight 36 proteins that exhibit the largest expression differences between matched tumor and non-tumor tissues. The concentrations of twenty proteins increased and sixteen decreased in tumor tissue, thirteen of which are novel for NSCLC. NSCLC tissue biomarkers identified here overlap with a core set identified in a large serum-based NSCLC study with SOMAscan. We show that large-scale comparative analysis of protein expression can be used to develop novel histochemical probes. As expected, relative differences in protein expression are greater in tissues than in serum. The combined results from tissue and serum present the most extensive view to date of the complex changes in NSCLC protein expression and provide important implications for diagnosis and treatment.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour3,4—at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.