To investigate how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we performed a data-driven analysis of nuclear growth variations in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days and developed open-source tools for image and data analysis and feature-based timelapse data exploration. Together, these data, tools, and workflows comprise a framework for systematic quantitative analysis of dynamics at individual and population levels, and the analysis further highlights important aspects to consider when interpreting timelapse data. We found that individual nuclear volume growth trajectories arise from short-timescale variations attributable to their spatiotemporal context within the colony. We identified a time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

To produce abundant cell culture samples to generate large, standardized image datasets of human induced pluripotent stem (hiPS) cells, we developed an automated workflow on a Hamilton STAR liquid handler system. This was developed specifically for culturing hiPS cell lines expressing fluorescently tagged proteins, which we have used to study the principles by which cells establish and maintain robust dynamic localization of cellular structures. This protocol includes all details for the maintenance, passage and seeding of cells, as well as Matrigel coating of 6-well plastic plates and 96-well optical-grade, glass plates. We also developed an automated image-based hiPS cell colony segmentation and feature extraction pipeline to streamline the process of predicting cell count and selecting wells with consistent morphology for high-resolution three-dimensional (3D) microscopy. The imaging samples produced with this protocol have been used to study the integrated intracellular organization and cell-to-cell variability of hiPS cells to train and develop deep learning-based label-free predictions from transmitted-light microscopy images and to develop deep learning-based generative models of single-cell organization. This protocol requires some experience with robotic equipment. However, we provide details and source code to facilitate implementation by biologists less experienced with robotics. The protocol is completed in less than 10 h with minimal human interaction. Overall, automation of our cell culture procedures increased our imaging samples’ standardization, reproducibility, scalability and consistency. It also reduced the need for stringent culturist training and eliminated culturist-to-culturist variability, both of which were previous pain points of our original manual pipeline workflow.

The epithelial to mesenchymal transition (EMT) is a widely studied but poorly defined state change due to the variety of ways in which it has been characterized in cells. There is a need for reproducible cell model systems that enable the integration and comparison of different types of measured observations of cells across many distinct cellular contexts. We present human induced pluripotent stem (hiPS) cells as such a model system by demonstrating its utility through a comparative analysis of hiPS cell-EMT in 2D and 3D cell culture geometries. We developed live-imaging-based assays to directly compare examples of changes in cell function (via migration timing), molecular components (via expression of marker proteins), organization (via reorganization of cell junctions), and environment (via dynamics of basement membrane) in the same experimental system. The EMT-related changes we measured occurred earlier in 2D colonies than in 3D lumenoids, likely due to differences in the basement membrane environments associated with 2D vs. 3D initial hiPS cell culture geometries. We have made the 449 60-hour-long 3D time-lapse movies and the associated tools used for analysis and visualization open-source and easily accessible as a resource for future work in this field. ### Competing Interest Statement The authors have declared no competing interest.

PDF file - 112K, Supplementary Figure legends, primer sequences and Western blot antibody details.

PDF file - 2418K, MYOD is expressed across different molecular subgroups of medulloblastoma.

PDF file - 4148K, Loss of MyoD does not influence its canonical targets in the myogenic differentiation program.

PDF file - 14123K, Mouse medulloblastomas with one allele of MyoD trend towards higher Ki67 index with reduction in total number of MyoD+ cells as well as cellular expression.

PDF file - 12536K, MyoD expression is higher in the tumor periphery compared to the inner core.

PDF file - 25456K, Comparative analysis of MyoD +/+; SmoA2, MyoD +/-; SmoA2 and MyoD-/-; SmoA2 mice.

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 determine 1,2 . Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour 3,4 —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.

PDF file - 1235K, MyoD is expressed in proliferating tumor cells in SmoA1, SmoA2 and Ptch conditional knock out medulloblastoma mouse models.

We introduce a new, robust method to map the numbers of proteins in living cells. The method can be applied to laser scanning, spinning disk, and lattice light-sheet microscopes in a robust, reproducible, and scalable fashion. The method uses calibrated EGFP solutions that are imaged with the appropriate microscope modality to create a calibration curve that is then applied to convert the fluorescence intensities from 3D microscope images into molecule numbers. We applied this method to human induced pluripotent stem cells in which proteins representing key cellular structures were endogenously tagged with mEGFP. We used the ratio of mEGFP-tagged proteins to total proteins to create 3D maps of live cells showing the density of total proteins measured in molecules per µm 3 . The method opens the door to new quantitative single cell analyses of cellular protein numbers in the context of single cell gene expression, associations with cellular complexes, and changes in cellular behaviors. The method is capable of quantifying protein numbers, over three orders of magnitude, in the cytoplasm or within various cellular structures while offering the unique advantages of each microscopy modality.

We developed a scalable, robust method to quantify protein concentrations in live, single cells in 3D acquired across microscope platforms. First, the EGFP concentrations in a standard curve are quantified using fluorescence correlation spectroscopy (FCS). Second, the EGFP standard curve is imaged at the same settings used to determine live cell protein concentrations, in order to create an intensity-concentration calibration curve. This calibration curve is then applied to the cell images to transform their relative fluorescence intensities into absolute protein concentrations. Using human induced pluripotent stem cells (hiPSCs) with different expression levels of cytoplasmic mEGFP, we demonstrate that our approach is comparable to established methods such as point-FCS and immunoblots. In contrast to point-FCS and immunoblotting which are limited to small, 2D sections in single cells or bulk analysis, respectively, our approach is compatible with 3D, single cell live imaging. Furthermore, our method can be applied across microscope modalities (laser scanning confocal, spinning disk and lattice light sheet microscopes) when platform-specific parameters such as detection limits, detectors or camera behaviors and field of view illumination profiles, are carefully considered. In a pilot study, we quantified protein concentrations for 15 hiPSC lines from the Allen Cell Collection (https://www.allencell.org/cell-catalog.html), in which key organelles or structures were endogenously tagged with mEGFP. For lines with mono-allelic expression of the tagged protein, the ratio of tagged to untagged protein determined by immunoblotting was used as correction factor to quantify total (tagged and untagged) cellular protein concentrations. By combining the quantification of key proteins in the cells with structural information and/or transcriptomics, we hope to create valuable datasets for quantifying, predicting and modeling cellular behaviors and states.

Our goal is to identify and understand cellular behaviors using 3D live imaging of cell organization. To do this, we image human inducible pluripotent stem cell (hiPSC) lines expressing fluorescently tagged protein representing specific cellular organelles and structures. To produce large numbers of standardized cell images, we developed an automated hiPSC culture procedure, to maintain, passage and Matrigel coat 6-well plastic plates and 96-well glass plates compatible with high-resolution 3D microscopy. Here we describe this system including optimization procedures and specific values for plate movement, angle of tips, speed of aspiration and dispense, seeding strategies and timing of every step. We validated this approach through a side-by-side comparison of quality control results obtained from manual and automated methods. Additionally, we developed an automated image-based colony segmentation and feature extraction pipeline to predict cell count and select wells with consistent morphology for high resolution 3D microscopy.

We introduce a new, robust method to map the numbers of proteins in living cells. The method can be applied to laser scanning, spinning disk, and lattice light-sheet microscopes in a robust, reproducible, and scalable fashion. The method uses calibrated EGFP solutions that are imaged with the appropriate microscope modality to create a calibration curve that is then applied to convert the fluorescence intensities from 3D microscope images into molecule numbers. We applied this method to human induced pluripotent stem cells in which proteins representing key cellular structures were endogenously tagged with mEGFP. We used the ratio of mEGFP-tagged proteins to total proteins to create 3D maps of live cells showing the density of total proteins measured in molecules per µm3. The method opens the door to new quantitative single cell analyses of cellular protein numbers in the context of single cell gene expression, associations with cellular complexes, and changes in cellular behaviors. The method is capable of quantifying protein numbers, over three orders of magnitude, in the cytoplasm or within various cellular structures while offering the unique advantages of each microscopy modality.### Competing Interest StatementThe authors have declared no competing interest.

Summary Despite the intimate link between cell organization and function, the principles underlying intracellular organization and the relation between organization, gene expression and phenotype are not well understood. We address this by creating a benchmark for mean cell organization and the natural range of cell-to-cell variation. This benchmark can be used for comparison to other normal or abnormal cell states. To do this, we developed a reproducible microscope imaging pipeline to generate a high-quality dataset of 3D, high-resolution images of over 200,000 live cells from 25 isogenic human induced pluripotent stem cell (hiPSC) lines from the Allen Cell Collection. Each line contains one fluorescently tagged protein, created via endogenous CRISPR/Cas9 gene editing, representing a key cellular structure or organelle. We used these images to develop a new multi-part and generalizable analysis approach of the locations, amounts, and variation of these 25 cellular structures. Taking an integrated approach, we found that both the extent to which a structure’s individual location varied (“stereotypy”) and the extent to which the structure localized relative to all the other cellular structures (“concordance”) were robust to a wide range of cell shape variation, from flatter to taller, smaller to larger, or less to more polarized cells. We also found that these cellular structures varied greatly in how their volumes scaled with cell and nuclear size. These analyses create a data-driven set of quantitative rules for the locations, amounts, and variation of 25 cellular structures within the hiPSC as a normal baseline for cell organization.

The mission of the Allen Institute for Cell Science is to understand and predict cell behaviors. The current project aims to create a stem cell state space by conjoining genomic, imaging, and phenotypic data from cells. We tagged most major molecular machineries (organelles, membranes and cytoskeleton) of human induced pluripotent stem cells (hiPSCs) with fluorescent proteins, and acquired large, reproducible datasets of each cell line for model input using an automated spinning-disk microscopy pipeline (www.allencell.org). We used the CRISPR/Cas9 system to fuse the fluorescent proteins directly to the protein of interest under the endogenous promoter. Therefore, the measured intensity of fluorescence emission is proportional to the protein distribution, localization, and dynamics of individual cells. To improve the accuracy of the integrated cell models we are building, we collected data with higher spatial resolution and measured absolute protein concentrations. We increased the resolution by about 1.7x using the Zeiss LSM 880 confocal microscope equipped with an Airy fast detector based on pixel reassignment. We created intensity/concentration calibration standards using cytosolic eGFP, cell-lysate, as well as purified eGFP in solution. We calibrated the concentration standards with FCS (Fluorescence Correlation Spectroscopy). The concentration standards allow quantitative comparison of data collected on different systems. We validated these approaches with quantitative Western blots, ELISA, and UV/VIS spectroscopy. We will discuss the advantages and limitations of the different standards, as well as the limitations, corrections, and controls necessary because of mono-allelic expression of eGFP-tagged proteins, inactive eGFP, and environmental changes of eGFP brightness. These results will add additional, quantitative constraints to our integrated models based on concentrations as well as finer structural detail due to the improved resolution.

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.

Spontaneously occurring soft tissue sarcoma (STS) is relatively common in canine cancer patients. Because of the similarities to human disease, canine STSs are a valuable and readily available resource for the study of new therapeutics. In this study, a canine patient-derived xenograft (PDX) model, CDX-STS2, was established. The CDX-STS2 model was engrafted and expanded for systemic administration studies with chemotherapeutic agents commonly used to treat STS, including doxorubicin, docetaxel and gemcitabine. Immunohistochemistry for drug-specific biomarkers and tumour growth measurement revealed tumour sensitivity to doxorubicin and docetaxel, whereas gemcitabine had no effect on tumour growth. Although many human PDX tumour models have been established, relatively few canine PDX models have been reported to date. CDX-STS2 represents a new STS PDX research model of canine origin that will be useful in bridging preclinical research with clinical studies of STS in pet dogs.

The vision of a precision medicine-guided approach to novel cancer drug development is challenged by high intratumor heterogeneity and interpatient diversity. This complexity is rarely modeled accurately during preclinical drug development, hampering predictions of clinical drug efficacy. To address this issue, we developed Comparative In Vivo Oncology (CIVO) arrayed microinjection technology to test tumor responsiveness to simultaneous microdoses of multiple drugs directly in a patient's tumor. Here, in a study of 18 canine patients with soft tissue sarcoma (STS), CIVO captured complex, patient-specific tumor responses encompassing both cancer cells and multiple immune infiltrates following localized exposure to different chemotherapy agents. CIVO also classified patient-specific tumor resistance to the most effective agent, doxorubicin, and further enabled assessment of a preclinical autophagy inhibitor, PS-1001, to reverse doxorubicin resistance. In a CIVO-identified subset of doxorubicin-resistant tumors, PS-1001 resulted in enhanced antitumor activity, increased infiltration of macrophages, and skewed this infiltrate toward M1 polarization. The ability to evaluate and cross-compare multiple drugs and drug combinations simultaneously in living tumors and across a diverse immunocompetent patient population may provide a foundation from which to make informed drug development decisions. This method also represents a viable functional approach to complement current precision oncology strategies. Cancer Res; 77(11); 2869-80. ©2017 AACR.