Morgridge Institute for Research

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics. The 1000 Genomes Project has sought to comprehensively catalogue human genetic variation across populations, providing a valuable public genomic resource. The data obtained so far have found applications ranging from association studies and fine mapping studies to the filtering of likely neutral variants in rare-disease cohorts. The authors now report on the final phase of the project, phase 3, which covers previously uncharacterized areas of human genetic diversity in terms of the populations sampled and categories of characterized variation. The sample now includes more than 2,500 individuals from 26 global populations, with low coverage whole-genome and deep exome sequencing, as well as dense microarray genotyping. They find that while most common variants are shared across populations, rarer variants are often restricted to closely related populations. The authors also demonstrate the use of the phase 3 dataset as a reference panel for imputation to improve the resolution in genetic association studies.

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations. This report from the 1000 Genomes Project describes the genomes of 1,092 individuals from 14 human populations, providing a resource for common and low-frequency variant analysis in individuals from diverse populations; hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites, can be found in each individual. This report by the 1000 Genomes Project describes the genomes of 1,092 individuals from 14 human populations, providing a resource for common and low-frequency variant analysis in individuals from diverse populations. Integrative analyses reveal profiles of rare and common variants in different populations. The frequencies of rare variants vary across biological pathways, and hundreds of rare, non-coding variants at conserved sites — such as changes disrupting transcription-factor motifs — can be established for each individual.

BACKGROUND: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. RESULTS: We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. CONCLUSIONS: Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

We present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools. The current capabilities of TrackMate are presented in the context of three different biological problems. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. Second, we investigate the recruitment of NEMO (NF-κB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis. Finally, we validate the use of TrackMate for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.

Reprogramming differentiated human cells to induced pluripotent stem (iPS) cells has applications in basic biology, drug development, and transplantation. Human iPS cell derivation previously required vectors that integrate into the genome, which can create mutations and limit the utility of the cells in both research and clinical applications. We describe the derivation of human iPS cells with the use of nonintegrating episomal vectors. After removal of the episome, iPS cells completely free of vector and transgene sequences are derived that are similar to human embryonic stem (ES) cells in proliferative and developmental potential. These results demonstrate that reprogramming human somatic cells does not require genomic integration or the continued presence of exogenous reprogramming factors and removes one obstacle to the clinical application of human iPS cells.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Higher-order chromatin structure is emerging as an important regulator of gene expression. Although dynamic chromatin structures have been identified in the genome, the full scope of chromatin dynamics during mammalian development and lineage specification remains to be determined. By mapping genome-wide chromatin interactions in human embryonic stem (ES) cells and four human ES-cell-derived lineages, we uncover extensive chromatin reorganization during lineage specification. We observe that although self-associating chromatin domains are stable during differentiation, chromatin interactions both within and between domains change in a striking manner, altering 36% of active and inactive chromosomal compartments throughout the genome. By integrating chromatin interaction maps with haplotype-resolved epigenome and transcriptome data sets, we find widespread allelic bias in gene expression correlated with allele-biased chromatin states of linked promoters and distal enhancers. Our results therefore provide a global view of chromatin dynamics and a resource for studying long-range control of gene expression in distinct human cell lineages. An analysis of genome-wide chromatin interactions during human embryonic stem cell differentiation reveals changes in chromatic organization and simultaneously identifies allele-resolved chromatin structure and differences in gene expression during differentiation. Higher-order chromatin structures are among the factors influencing gene expression, although how these structures evolve during differentiation and lineage specification in mammalian systems is still unclear. Bing Ren and colleagues have mapped the differences in genome-wide chromatin interactions between human embryonic stem cells and their differentiated progeny. They delineate biases in allelic gene expression that correlate with allele-biased chromatin interactions between distal enhancers and proximal promoters.

MOTIVATION: Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others. RESULTS: Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes. AVAILABILITY AND IMPLEMENTATION: An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/kendzior/EBSEQ/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Human induced pluripotent stem (iPS) cells hold great promise for cardiovascular research and therapeutic applications, but the ability of human iPS cells to differentiate into functional cardiomyocytes has not yet been demonstrated. The aim of this study was to characterize the cardiac differentiation potential of human iPS cells generated using OCT4, SOX2, NANOG, and LIN28 transgenes compared to human embryonic stem (ES) cells. The iPS and ES cells were differentiated using the embryoid body (EB) method. The time course of developing contracting EBs was comparable for the iPS and ES cell lines, although the absolute percentages of contracting EBs differed. RT-PCR analyses of iPS and ES cell-derived cardiomyocytes demonstrated similar cardiac gene expression patterns. The pluripotency genes OCT4 and NANOG were downregulated with cardiac differentiation, but the downregulation was blunted in the iPS cell lines because of residual transgene expression. Proliferation of iPS and ES cell-derived cardiomyocytes based on 5-bromodeoxyuridine labeling was similar, and immunocytochemistry of isolated cardiomyocytes revealed indistinguishable sarcomeric organizations. Electrophysiology studies indicated that iPS cells have a capacity like ES cells for differentiation into nodal-, atrial-, and ventricular-like phenotypes based on action potential characteristics. Both iPS and ES cell-derived cardiomyocytes exhibited responsiveness to beta-adrenergic stimulation manifest by an increase in spontaneous rate and a decrease in action potential duration. We conclude that human iPS cells can differentiate into functional cardiomyocytes, and thus iPS cells are a viable option as an autologous cell source for cardiac repair and a powerful tool for cardiovascular research.

Abstract Motivation: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically. Results: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed. Availability: An initial C++ implementation of our method that was used for the results presented in this article is available at http://deweylab.biostat.wisc.edu/rsem. Contact: cdewey@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics on

SIGNIFICANCE: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy. AIM: We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications. APPROACH: This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed. CONCLUSIONS: FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems.

Genome engineering in human pluripotent stem cells (hPSCs) holds great promise for biomedical research and regenerative medicine. Recently, an RNA-guided, DNA-cleaving interference pathway from bacteria [the type II clustered, regularly interspaced, short palindromic repeats (CRISPR)-CRISPR-associated (Cas) pathway] has been adapted for use in eukaryotic cells, greatly facilitating genome editing. Only two CRISPR-Cas systems (from Streptococcus pyogenes and Streptococcus thermophilus), each with their own distinct targeting requirements and limitations, have been developed for genome editing thus far. Furthermore, limited information exists about homology-directed repair (HDR)-mediated gene targeting using long donor DNA templates in hPSCs with these systems. Here, using a distinct CRISPR-Cas system from Neisseria meningitidis, we demonstrate efficient targeting of an endogenous gene in three hPSC lines using HDR. The Cas9 RNA-guided endonuclease from N. meningitidis (NmCas9) recognizes a 5'-NNNNGATT-3' protospacer adjacent motif (PAM) different from those recognized by Cas9 proteins from S. pyogenes and S. thermophilus (SpCas9 and StCas9, respectively). Similar to SpCas9, NmCas9 is able to use a single-guide RNA (sgRNA) to direct its activity. Because of its distinct protospacer adjacent motif, the N. meningitidis CRISPR-Cas machinery increases the sequence contexts amenable to RNA-directed genome editing.

BACKGROUND: Human pluripotent stem cells offer the best available model to study the underlying cellular and molecular mechanisms of human embryonic lineage specification. However, it is not fully understood how individual stem cells exit the pluripotent state and transition towards their respective progenitor states. RESULTS: Here, we analyze the transcriptomes of human embryonic stem cell-derived lineage-specific progenitors by single-cell RNA-sequencing (scRNA-seq). We identify a definitive endoderm (DE) transcriptomic signature that leads us to pinpoint a critical time window when DE differentiation is enhanced by hypoxia. The molecular mechanisms governing the emergence of DE are further examined by time course scRNA-seq experiments, employing two new statistical tools to identify stage-specific genes over time (SCPattern) and to reconstruct the differentiation trajectory from the pluripotent state through mesendoderm to DE (Wave-Crest). Importantly, presumptive DE cells can be detected during the transitory phase from Brachyury (T) (+) mesendoderm toward a CXCR4 (+) DE state. Novel regulators are identified within this time window and are functionally validated on a screening platform with a T-2A-EGFP knock-in reporter engineered by CRISPR/Cas9. Through loss-of-function and gain-of-function experiments, we demonstrate that KLF8 plays a pivotal role modulating mesendoderm to DE differentiation. CONCLUSIONS: We report the analysis of 1776 cells by scRNA-seq covering distinct human embryonic stem cell-derived progenitor states. By reconstructing a differentiation trajectory at single-cell resolution, novel regulators of the mesendoderm transition to DE are elucidated and validated. Our strategy of combining single-cell analysis and genetic approaches can be applied to uncover novel regulators governing cell fate decisions in a variety of systems.

Differentiation methods for human induced pluripotent stem cells (hiPSCs) typically yield progeny from multiple tissue lineages, limiting their use for drug testing and autologous cell transplantation. In particular, early retina and forebrain derivatives often intermingle in pluripotent stem cell cultures, owing to their shared ancestry and tightly coupled development. Here, we demonstrate that three-dimensional populations of retinal progenitor cells (RPCs) can be isolated from early forebrain populations in both human embryonic stem cell and hiPSC cultures, providing a valuable tool for developmental, functional, and translational studies. Using our established protocol, we identified a transient population of optic vesicle (OV)-like structures that arose during a time period appropriate for normal human retinogenesis. These structures were independently cultured and analyzed to confirm their multipotent RPC status and capacity to produce physiologically responsive retinal cell types, including photoreceptors and retinal pigment epithelium (RPE). We then applied this method to hiPSCs derived from a patient with gyrate atrophy, a retinal degenerative disease affecting the RPE. RPE generated from these hiPSCs exhibited a disease-specific functional defect that could be corrected either by pharmacological means or following targeted gene repair. The production of OV-like populations from human pluripotent stem cells should facilitate the study of human retinal development and disease and advance the use of hiPSCs in personalized medicine.

Induced pluripotent stem cells (iPSCs) provide an unprecedented opportunity for modeling of human diseases in vitro, as well as for developing novel approaches for regenerative therapy based on immunologically compatible cells. In this study, we employed an OP9 differentiation system to characterize the hematopoietic and endothelial differentiation potential of seven human iPSC lines obtained from human fetal, neonatal, and adult fibroblasts through reprogramming with POU5F1, SOX2, NANOG, and LIN28 and compared it with the differentiation potential of five human embryonic stem cell lines (hESC, H1, H7, H9, H13, and H14). Similar to hESCs, all iPSCs generated CD34(+)CD43(+) hematopoietic progenitors and CD31(+)CD43(-) endothelial cells in coculture with OP9. When cultured in semisolid media in the presence of hematopoietic growth factors, iPSC-derived primitive blood cells formed all types of hematopoietic colonies, including GEMM colony-forming cells. Human induced pluripotent cells (hiPSCs)-derived CD43(+) cells could be separated into the following phenotypically defined subsets of primitive hematopoietic cells: CD43(+)CD235a(+)CD41a(+/-) (erythro-megakaryopoietic), lin(-)CD34(+)CD43(+)CD45(-) (multipotent), and lin(-)CD34(+)CD43(+)CD45(+) (myeloid-skewed) cells. Although we observed some variations in the efficiency of hematopoietic differentiation between different hiPSCs, the pattern of differentiation was very similar in all seven tested lines obtained through reprogramming of human fetal, neonatal, or adult fibroblasts with three or four genes. Although several issues remain to be resolved before iPSC-derived blood cells can be administered to humans for therapeutic purposes, patient-specific iPSCs can already be used for characterization of mechanisms of blood diseases and for identification of molecules that can correct affected genetic networks.

RATIONALE: Cardiomyocytes (CMs) differentiated from human pluripotent stem cells (PSCs) are increasingly being used for cardiovascular research, including disease modeling, and hold promise for clinical applications. Current cardiac differentiation protocols exhibit variable success across different PSC lines and are primarily based on the application of growth factors. However, extracellular matrix is also fundamentally involved in cardiac development from the earliest morphogenetic events, such as gastrulation. OBJECTIVE: We sought to develop a more effective protocol for cardiac differentiation of human PSCs by using extracellular matrix in combination with growth factors known to promote cardiogenesis. METHODS AND RESULTS: PSCs were cultured as monolayers on Matrigel, an extracellular matrix preparation, and subsequently overlayed with Matrigel. The matrix sandwich promoted an epithelial-to-mesenchymal transition as in gastrulation with the generation of N-cadherin-positive mesenchymal cells. Combining the matrix sandwich with sequential application of growth factors (Activin A, bone morphogenetic protein 4, and basic fibroblast growth factor) generated CMs with high purity (up to 98%) and yield (up to 11 CMs/input PSC) from multiple PSC lines. The resulting CMs progressively matured over 30 days in culture based on myofilament expression pattern and mitotic activity. Action potentials typical of embryonic nodal, atrial, and ventricular CMs were observed, and monolayers of electrically coupled CMs modeled cardiac tissue and basic arrhythmia mechanisms. CONCLUSIONS: Dynamic extracellular matrix application promoted epithelial-mesenchymal transition of human PSCs and complemented growth factor signaling to enable robust cardiac differentiation.

The emergence of the 2019 novel coronavirus has led to more than a pandemic—indeed, COVID-19 is spawning myriad other concerns as it rapidly marches around the globe. One of these concerns is a surge of misinformation, which we argue should be viewed as a risk in its own right, and to which insights from decades of risk communication research must be applied. Further, when the subject of misinformation is itself a risk, as in the case of COVID-19, we argue for the utility of viewing the problem as a multi-layered risk communication problem. In such circumstances, misinformation functions as a meta-risk that interacts with and complicates publics’ perceptions of the original risk. Therefore, as the COVID-19 “misinfodemic” intensifies, risk communication research should inform the efforts of key risk communicators. To this end, we discuss the implications of risk research for efforts to fact-check COVID-19 misinformation and offer practical recommendations.

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.