Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.

A reduced-dimensionality approach to uncovering dyadic modes of body motion in conversations

Face-to-face conversations are central to human communication and a fascinating example of joint action. Beyond verbal content, one of the primary ways in which information is conveyed in conversations is body language. Body motion in natural conversations has been difficult to study precisely due to the large number of coordinates at play. There is need for fresh approaches to analyze and understand the data, in order to ask whether dyads show basic building blocks of coupled motion. Here we present a method for analyzing body motion during joint action using depth-sensing cameras, and use it to analyze a sample of scientific conversations. Our method consists of three steps: defining modes of body motion of individual participants, defining dyadic modes made of combinations of these individual modes, and lastly defining motion motifs as dyadic modes that occur significantly more often than expected given the single-person motion statistics. As a proof-of-concept, we analyze the motion of 12 dyads of scientists measured using two Microsoft Kinect cameras. In our sample, we find that out of many possible modes, only two were motion motifs: synchronized parallel torso motion in which the participants swayed from side to side in sync, and still segments where neither person moved. We find evidence of dyad individuality in the use of motion modes. For a randomly selected subset of 5 dyads, this individuality was maintained for at least 6 months. The present approach to simplify complex motion data and to define motion motifs may be used to understand other joint tasks and interactions. The analysis tools developed here and the motion dataset are publicly available.

Dynamic Proteomics: a database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells

Recent advances allow tracking the levels and locations of a thousand proteins in individual living human cells over time using a library of annotated reporter cell clones (LARC). This library was created by Cohen et al. to study the proteome dynamics of a human lung carcinoma cell-line treated with an anti-cancer drug. Here, we report the Dynamic Proteomics database for the proteins studied by Cohen et al. Each cell-line clone in LARC has a protein tagged with yellow fluorescent protein, expressed from its endogenous chromosomal location, under its natural regulation. The Dynamic Proteomics interface facilitates searches for genes of interest, downloads of protein fluorescent movies and alignments of dynamics following drug addition. Each protein in the database is displayed with its annotation, cDNA sequence, fluorescent images and movies obtained by the time-lapse microscopy. The protein dynamics in the database represents a quantitative trace of the protein fluorescence levels in nucleus and cytoplasm produced by image analysis of movies over time. Furthermore, a sequence analysis provides a search and comparison of up to 50 input DNA sequences with all cDNAs in the library. The raw movies may be useful as a benchmark for developing image analysis tools for individual-cell dynamic-proteomics. The database is available at http://www.dynamicproteomics.net/.

Dynamic proteomics of individual cancer cells in response to a drug

Why do seemingly identical cells respond differently to a drug? To address this, we studied the dynamics and variability of the protein response of human cancer cells to a chemotherapy drug, camptothecin. We present a dynamic-proteomics approach that measures the levels and locations of nearly 1000 different endogenously tagged proteins in individual living cells at high temporal resolution. All cells show rapid translocation of proteins specific to the drug mechanism, including the drug target (topoisomerase-1), and slower, wide-ranging temporal waves of protein degradation and accumulation. However, the cells differ in the behavior of a subset of proteins. We identify proteins whose dynamics differ widely between cells, in a way that corresponds to the outcomes-cell death or survival. This opens the way to understanding molecular responses to drugs in individual cells.

Generation of a fluorescently labeled endogenous protein library in living human cells

We present a protocol to tag proteins expressed from their endogenous chromosomal locations in individual mammalian cells using central dogma tagging. The protocol can be used to build libraries of cell clones, each expressing one endogenous protein tagged with a fluorophore such as the yellow fluorescent protein. Each round of library generation produces 100-200 cell clones and takes about 1 month. The protocol integrates procedures for high-throughput single-cell cloning using flow cytometry, high-throughput cDNA generation and 3' rapid amplification of cDNA ends, semi-automatic protein localization screening using fluorescent microscopy and freezing cells in 96-well format.

High-throughput screening of cellular features using high-resolution light-microscopy; Application for profiling drug effects on cell adhesion

High-resolution light-microscopy and high-throughput screening are two essential methodologies for characterizing cellular phenotypes. Optimally combining these methodologies in cell-based screening to test detailed molecular and cellular responses to multiple perturbations constitutes a major challenge. Here we describe the development and application of a screening microscope platform that automatically acquires and interprets sub-micron resolution images at fast rates. The analysis pipeline is based on the quantification of multiple subcellular features and statistical comparisons of their distributions in treated vs. control cells. Using this platform, we screened 2200 natural extracts for their effects on the fine structure and organization of focal adhesions. This screen identified 15 effective extracts whose fractionation and characterization were further analyzed using the same microscope system. The significance of combining resolution, throughput and multi-parametric analyses for biomedical research and drug discovery is discussed.

Variability and memory of protein levels in human cells

Protein expression is a stochastic process that leads to phenotypic variation among cells. The cell-cell distribution of protein levels in microorganisms has been well characterized but little is known about such variability in human cells. Here, we studied the variability of protein levels in human cells, as well as the temporal dynamics of this variability, and addressed whether cells with higher than average protein levels eventually have lower than average levels, and if so, over what timescale does this mixing occur. We measured fluctuations over time in the levels of 20 endogenous proteins in living human cells, tagged by the gene for yellow fluorescent protein at their chromosomal loci. We found variability with a standard deviation that ranged, for different proteins, from about 15% to 30% of the mean. Mixing between high and low levels occurred for all proteins, but the mixing time was longer than two cell generations (more than 40 h) for many proteins. We also tagged pairs of proteins with two colours, and found that the levels of proteins in the same biological pathway were far more correlated than those of proteins in different pathways. The persistent memory for protein levels that we found might underlie individuality in cell behaviour and could set a timescale needed for signals to affect fully every member of a cell population.

Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins

We examined cell cycle-dependent changes in the proteome of human cells by systematically measuring protein dynamics in individual living cells. We used time-lapse microscopy to measure the dynamics of a random subset of 20 nuclear proteins, each tagged with yellow fluorescent protein (YFP) at its endogenous chromosomal location. We synchronized the cells in silico by aligning protein dynamics in each cell between consecutive divisions. We observed widespread (40%) cell-cycle dependence of nuclear protein levels and detected previously unknown cell cycle-dependent localization changes. This approach to dynamic proteomics can aid in discovery and accurate quantification of the extensive regulation of protein concentration and localization in individual living cells.

Oscillations and variability in the p53 system

Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time.

Laser autofocusing system for high-resolution cell biological imaging

Automated acquisition of high resolution, light microscope images of cells is becoming a common requirement in modern proteomic and cellomic research. A prerequisite for such microscopy is fine focus tuning, commonly optimized by multiple exposures, followed by image sharpness analysis. We describe here an extremely fast and accurate laser autofocusing system with distinct advantages for large-scale cell-based screening.

Development and application of automatic high-resolution light microscopy for cell-based screens

Large-scale microscopy-based screens offer compelling advantages for assessing the effects of genetic and pharmacological modulations on a wide variety of cellular features. However, development of such assays is often confronted by an apparent conflict between the need for high throughput, which usually provides limited information on a large number of samples, and a high-content approach, providing detailed information on each sample. This chapter describes a novel high-resolution screening (HRS) platform that is able to acquire large sets of data at a high rate and light microscope resolution using specific "reporter cells," cultured in multiwell plates. To harvest extensive morphological and molecular information in these automated screens, we have constructed a general analysis pipeline that is capable of assigning scores to multiparameter-based comparisons between treated cells and controls. This chapter demonstrates the structure of this system and its application for several research projects, including screening of chemical compound libraries for their effect on cell adhesion, discovery of novel cytoskeletal genes, discovery of cell migration-related genes, and a siRNA screen for perturbation of cell adhesion.