Allen Institute for Cell Science

BACKGROUND: Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. RESULTS: Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines. CONCLUSIONS: CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler's powerful computational tools in the coming years.

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.

We present a CRISPR/Cas9 genome-editing strategy to systematically tag endogenous proteins with fluorescent tags in human induced pluripotent stem cells (hiPSC). To date, we have generated multiple hiPSC lines with monoallelic green fluorescent protein tags labeling 10 proteins representing major cellular structures. The tagged proteins include alpha tubulin, beta actin, desmoplakin, fibrillarin, nuclear lamin B1, nonmuscle myosin heavy chain IIB, paxillin, Sec61 beta, tight junction protein ZO1, and Tom20. Our genome-editing methodology using Cas9/crRNA ribonuclear protein and donor plasmid coelectroporation, followed by fluorescence-based enrichment of edited cells, typically resulted in <0.1-4% homology-directed repair (HDR). Twenty-five percent of clones generated from each edited population were precisely edited. Furthermore, 92% (36/39) of expanded clonal lines displayed robust morphology, genomic stability, expression and localization of the tagged protein to the appropriate subcellular structure, pluripotency-marker expression, and multilineage differentiation. It is our conclusion that, if cell lines are confirmed to harbor an appropriate gene edit, pluripotency, differentiation potential, and genomic stability are typically maintained during the clonal line-generation process. The data described here reveal general trends that emerged from this systematic gene-tagging approach. Final clonal lines corresponding to each of the 10 cellular structures are now available to the research community.

Leukocytes and other amoeboid cells change shape as they move, forming highly dynamic, actin-filled pseudopods. Although we understand much about the architecture and dynamics of thin lamellipodia made by slow-moving cells on flat surfaces, conventional light microscopy lacks the spatial and temporal resolution required to track complex pseudopods of cells moving in three dimensions. We therefore employed lattice light sheet microscopy to perform three-dimensional, time-lapse imaging of neutrophil-like HL-60 cells crawling through collagen matrices. To analyze three-dimensional pseudopods we: (i) developed fluorescent probe combinations that distinguish cortical actin from dynamic, pseudopod-forming actin networks, and (ii) adapted molecular visualization tools from structural biology to render and analyze complex cell surfaces. Surprisingly, three-dimensional pseudopods turn out to be composed of thin (<0.75 µm), flat sheets that sometimes interleave to form rosettes. Their laminar nature is not templated by an external surface, but likely reflects a linear arrangement of regulatory molecules. Although we find that Arp2/3-dependent pseudopods are dispensable for three-dimensional locomotion, their elimination dramatically decreases the frequency of cell turning, and pseudopod dynamics increase when cells change direction, highlighting the important role pseudopods play in pathfinding.

Abstract 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.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Abstract A continuing challenge in quantitative cell biology is the accurate and robust 3D segmentation of structures of interest from fluorescence microscopy images in an automated, reproducible, and widely accessible manner for subsequent interpretable data analysis. We describe the Allen Cell and Structure Segmenter (Segmenter), a Python-based open source toolkit developed for 3D segmentation of cells and intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high-replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes. The Segmenter consists of two complementary elements, a classic image segmentation workflow with a restricted set of algorithms and parameters and an iterative deep learning segmentation workflow. We created a collection of 20 classic image segmentation workflows based on 20 distinct and representative intracellular structure localization patterns as a “lookup table” reference and starting point for users. The iterative deep learning workflow can take over when the classic segmentation workflow is insufficient. Two straightforward “human-in-the-loop” curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need for manual painting in 3D. The deep learning model architectures used in this toolkit were designed and tested specifically for 3D fluorescence microscope images and implemented as readable scripts. The Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher. We include two useful applications to demonstrate how we used the classic image segmentation and iterative deep learning workflows to solve more challenging 3D segmentation tasks. First, we introduce the ‘Training Assay’ approach, a new experimental-computational co-design concept to generate more biologically accurate segmentation ground truths. We combined the iterative deep learning workflow with three Training Assays to develop a robust, scalable cell and nuclear instance segmentation algorithm, which could achieve accurate target segmentation for over 98% of individual cells and over 80% of entire fields of view. Second, we demonstrate how to extend the lamin B1 segmentation model built from the iterative deep learning workflow to obtain more biologically accurate lamin B1 segmentation by utilizing multi-channel inputs and combining multiple ML models. The steps and workflows used to develop these algorithms are generalizable to other similar segmentation challenges. More information, including tutorials and code repositories, are available at allencell.org/segmenter.

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.

The bacteria that grow on methane aerobically (methanotrophs) support populations of non-methanotrophs in the natural environment by excreting methane-derived carbon. One group of excreted compounds are short-chain organic acids, generated in highest abundance when cultures are grown under O 2 -starvation. We examined this O 2 -starvation condition in the methanotroph Methylomicrobium buryatense 5GB1. The M. buryatense 5GB1 genome contains homologs for all enzymes necessary for a fermentative metabolism, and we hypothesize that a metabolic switch to fermentation can be induced by low-O 2 conditions. Under prolonged O 2 -starvation in a closed vial, this methanotroph increases the amount of acetate excreted about 10-fold, but the formate, lactate, and succinate excreted do not respond to this culture condition. In bioreactor cultures, the amount of each excreted product is similar across a range of growth rates and limiting substrates, including O 2 -limitation. A set of mutants were generated in genes predicted to be involved in generating or regulating excretion of these compounds and tested for growth defects, and changes in excretion products. The phenotypes and associated metabolic flux modeling suggested that in M. buryatense 5GB1, formate and acetate are excreted in response to redox imbalance. Our results indicate that even under O 2 -starvation conditions, M. buryatense 5GB1 maintains a metabolic state representing a combination of fermentation and respiration metabolism.

Abstract Background Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. Results Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines. Conclusions CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler’s powerful computational tools in the coming years.

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

Talin and vinculin are mechanosensitive proteins that are recruited early to integrin-based nascent adhesions (NAs). In two epithelial cell systems with well-delineated NA formation, we find these molecules concurrently recruited to the subclass of NAs maturing to focal adhesions. After the initial recruitment under minimal load, vinculin accumulates in maturing NAs at a ~ fivefold higher rate than in non-maturing NAs, and is accompanied by a faster traction force increase. We identify the R8 domain in talin, which exposes a vinculin-binding-site (VBS) in the absence of load, as required for NA maturation. Disruption of R8 domain function reduces load-free vinculin binding to talin, and reduces the rate of additional vinculin recruitment. Taken together, these data show that the concurrent recruitment of talin and vinculin prior to mechanical engagement with integrins is essential for the traction-mediated unfolding of talin, exposure of additional VBSs, further recruitment of vinculin, and ultimately, NA maturation.

During development, progenitors simultaneously activate one lineage while silencing another, a feature highly regulated in adult stem cells but derailed in cancers. Equipped to bind cognate motifs in closed chromatin, pioneer factors operate at these crossroads, but how they perform fate switching remains elusive. Here we tackle this question with SOX9, a master regulator that diverts embryonic epidermal stem cells (EpdSCs) into becoming hair follicle stem cells. By engineering mice to re-activate SOX9 in adult EpdSCs, we trigger fate switching. Combining epigenetic, proteomic and functional analyses, we interrogate the ensuing chromatin and transcriptional dynamics, slowed temporally by the mature EpdSC niche microenvironment. We show that as SOX9 binds and opens key hair follicle enhancers de novo in EpdSCs, it simultaneously recruits co-factors away from epidermal enhancers, which are silenced. Unhinged from its normal regulation, sustained SOX9 subsequently activates oncogenic transcriptional regulators that chart the path to cancers typified by constitutive SOX9 expression.

We describe a multistep method for endogenous tagging of transcriptionally silent genes in human induced pluripotent stem cells (hiPSCs). A monomeric EGFP (mEGFP) fusion tag and a constitutively expressed mCherry fluorescence selection cassette were delivered in tandem via homology-directed repair to five genes not expressed in hiPSCs but important for cardiomyocyte sarcomere function: TTN, MYL7, MYL2, TNNI1, and ACTN2. CRISPR/Cas9 was used to deliver the selection cassette and subsequently mediate its excision via microhomology-mediated end-joining and non-homologous end-joining. Most excised clones were effectively tagged, and all properly tagged clones expressed the mEGFP fusion protein upon differentiation into cardiomyocytes, allowing live visualization of these cardiac proteins at the sarcomere. This methodology provides a broadly applicable strategy for endogenously tagging transcriptionally silent genes in hiPSCs, potentially enabling their systematic and dynamic study during differentiation and morphogenesis.

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

A modern day light microscope has evolved from a tool devoted to making primarily empirical observations to what is now a sophisticated , quantitative device that is an integral part of both physical and life science research. Nowadays, microscopes are found in nearly every experimental laboratory. However, despite their prevalent use in capturing and quantifying scientific phenomena, neither a thorough understanding of the principles underlying quantitative imaging techniques nor appropriate knowledge of how to calibrate, operate and maintain microscopes can be taken for granted. This is clearly demonstrated by the well-documented and widespread difficulties that are routinely encountered in evaluating acquired data and reproducing scientific experiments. Indeed, studies have shown that more than 70% of researchers have tried and failed to repeat another scientist's experiments, while more than half have even failed to reproduce their own experiments. One factor behind the reproducibility crisis of experiments published in scientific journals is the frequent underreporting of imaging methods caused by a lack of awareness and/or a lack of knowledge of the applied technique. Whereas quality control procedures for some methods used in biomedical research, such as genomics (e.g. DNA sequencing, RNA-seq) or cytometry, have been introduced (e.g. ENCODE), this issue has not been tackled for optical microscopy instrumentation and images. Although many calibration standards and protocols have been published, there is a lack of awareness and agreement on common standards and guidelines for quality assessment and reproducibility. In April 2020, the QUality Assessment and REProducibility for instruments and images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper (1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; (2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers and observers of such; (3) outlines the current actions of the QUAREP-LiMi initiative and (4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics.

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

The Pharmacogene Variation Consortium (PharmVar) catalogs star (*) allele nomenclature for the polymorphic human CYP3A5 gene. Genetic variation within the CYP3A5 gene locus impacts the metabolism of several clinically important drugs, including the immunosuppressants tacrolimus, sirolimus, cyclosporine, and the benzodiazepine midazolam. Variable CYP3A5 activity is of clinical importance regarding tacrolimus metabolism. This GeneFocus provides a CYP3A5 gene summary with a focus on aspects regarding standardized nomenclature. In addition, this review also summarizes recent changes and updates, including the retirement of several allelic variants and provides an overview of how PharmVar CYP3A5 star allele nomenclature is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.