Anne E. Carpenter

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

A Lentiviral RNAi Library for Human and Mouse Genes Applied to an Arrayed Viral High-Content Screen

To enable arrayed or pooled loss-of-function screens in a wide range of mammalian cell types, including primary and nondividing cells, we are developing lentiviral short hairpin RNA (shRNA) libraries targeting the human and murine genomes. The libraries currently contain 104,000 vectors, targeting each of 22,000 human and mouse genes with multiple sequence-verified constructs. To test the utility of the library for arrayed screens, we developed a screen based on high-content imaging to identify genes required for mitotic progression in human cancer cells and applied it to an arrayed set of 5,000 unique shRNA-expressing lentiviruses that target 1,028 human genes. The screen identified several known and approximately 100 candidate regulators of mitotic progression and proliferation; the availability of multiple shRNAs targeting the same gene facilitated functional validation of putative hits. This work provides a widely applicable resource for loss-of-function screens, as well as a roadmap for its application to biological discovery.

mTOR Complex 1 Regulates Lipin 1 Localization to Control the SREBP Pathway

The nutrient- and growth factor-responsive kinase mTOR complex 1 (mTORC1) regulates many processes that control growth, including protein synthesis, autophagy, and lipogenesis. Through unknown mechanisms, mTORC1 promotes the function of SREBP, a master regulator of lipo- and sterolgenic gene transcription. Here, we demonstrate that mTORC1 regulates SREBP by controlling the nuclear entry of lipin 1, a phosphatidic acid phosphatase. Dephosphorylated, nuclear, catalytically active lipin 1 promotes nuclear remodeling and mediates the effects of mTORC1 on SREBP target gene, SREBP promoter activity, and nuclear SREBP protein abundance. Inhibition of mTORC1 in the liver significantly impairs SREBP function and makes mice resistant, in a lipin 1-dependent fashion, to the hepatic steatosis and hypercholesterolemia induced by a high-fat and -cholesterol diet. These findings establish lipin 1 as a key component of the mTORC1-SREBP pathway.

CellProfiler™: free, versatile software for automated biological image analysis

Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.

In germ cells of mouse embryonic ovaries, the decision to enter meiosis precedes premeiotic DNA replication

The transition from mitosis to meiosis is a defining juncture in the life cycle of sexually reproducing organisms. In yeast, the decision to enter meiosis is made before the single round of DNA replication that precedes the two meiotic divisions. We present genetic evidence of an analogous decision point in the germ line of a multicellular organism. The mouse Stra8 gene is expressed in germ cells of embryonic ovaries, where meiosis is initiated, but not in those of embryonic testes, where meiosis does not begin until after birth. Here we report that in female embryos lacking Stra8 gene function, the early, mitotic development of germ cells is normal, but these cells then fail to undergo premeiotic DNA replication, meiotic chromosome condensation, cohesion, synapsis and recombination. Combined with previous findings, these genetic data suggest that active differentiation of ovarian germ cells commences at a regulatory point upstream of premeiotic DNA replication.

Biological imaging software tools

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

Systematic genome-wide screens of gene function

By using genome information to create tools for perturbing gene function, it is now possible to undertake systematic genome-wide functional screens that examine the contribution of every gene to a biological process. The directed nature of these experiments contrasts with traditional methods, in which random mutations are induced and the resulting mutants are screened for various phenotypes. The first genome-wide functional screens in Caenorhabditis elegans and Drosophila melanogaster have recently been published, and screens in human cells will soon follow. These high-throughput techniques promise the rapid annotation of genomes with high-quality information about the biological function of each gene.

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.

An algorithm-based topographical biomaterials library to instruct cell fate

It is increasingly recognized that material surface topography is able to evoke specific cellular responses, endowing materials with instructive properties that were formerly reserved for growth factors. This opens the window to improve upon, in a cost-effective manner, biological performance of any surface used in the human body. Unfortunately, the interplay between surface topographies and cell behavior is complex and still incompletely understood. Rational approaches to search for bioactive surfaces will therefore omit previously unperceived interactions. Hence, in the present study, we use mathematical algorithms to design nonbiased, random surface features and produce chips of poly(lactic acid) with 2,176 different topographies. With human mesenchymal stromal cells (hMSCs) grown on the chips and using high-content imaging, we reveal unique, formerly unknown, surface topographies that are able to induce MSC proliferation or osteogenic differentiation. Moreover, we correlate parameters of the mathematical algorithms to cellular responses, which yield novel design criteria for these particular parameters. In conclusion, we demonstrate that randomized libraries of surface topographies can be broadly applied to unravel the interplay between cells and surface topography and to find improved material surfaces.

Cell microarrays and RNA interference chip away at gene function

The recent development of cell microarrays offers the potential to accelerate high-throughput functional genetic studies. The widespread use of RNA interference (RNAi) has prompted several groups to fabricate RNAi cell microarrays that make possible discrete, in-parallel transfection with thousands of RNAi reagents on a microarray slide. Though still a budding technology, RNAi cell microarrays promise to increase the efficiency, economy and ease of genome-wide RNAi screens in metazoan cells.