Colin Clarke

Bio: Colin Clarke is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 1, co-authored 1 publications receiving 3916 citations.

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

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

Gene Expression Analysis of Adapted Insect Cells during Influenza VLP Production Using RNA-Sequencing

Abstract: Adaptive laboratory evolution has been used to improve production of influenza hemagglutinin (HA)-displaying virus-like particles (VLPs) in insect cells. However, little is known about the underlying biological mechanisms promoting higher HA-VLP expression in such adapted cell lines. In this article, we present a study of gene expression patterns associated with high-producer insect High Five cells adapted to neutral pH, in comparison to non-adapted cells, during expression of influenza HA-VLPs. RNA-seq shows a decrease in the amount of reads mapping to host cell genomes along infection, and an increase in those mapping to baculovirus and transgenes. A total of 1742 host cell genes were found differentially expressed between adapted and non-adapted cells throughout infection, 474 of those being either up- or down-regulated at both time points evaluated (12 and 24 h post-infection). Interestingly, while host cell genes were found up- and down-regulated in an approximately 1:1 ratio, all differentially expressed baculovirus genes were found to be down-regulated in infected adapted cells. Pathway analysis of differentially expressed genes revealed enrichment of ribosome biosynthesis and carbohydrate, amino acid, and lipid metabolism. In addition, oxidative phosphorylation and protein folding, sorting and degradation pathways were also found to be overrepresented. These findings contribute to our knowledge of biological mechanisms of insect cells during baculovirus-mediated transient expression and will assist the identification of potential engineering targets to increase recombinant protein production in the future.

Transcriptome analysis of Sf9 insect cells during production of recombinant Adeno‐associated virus

Abstract: The insect cell‐baculovirus expression vector system (IC‐BEVS) has emerged as an alternative time‐ and cost‐efficient production platform for recombinant Adeno‐associated virus (AAV) for gene therapy. However, a better understanding of the underlying biological mechanisms of IC‐BEVS is fundamental to further optimize this expression system toward increased product titer and quality. Here, gene expression of Sf9 insect cells producing recombinant AAV through a dual baculovirus expression system, with low multiplicity of infection (MOI), was profiled by RNA‐seq. An 8‐fold increase in reads mapping to either baculovirus or AAV transgene sequences was observed between 24 and 48 h post‐infection (hpi), confirming a take‐over of the host cell transcriptome by the baculovirus. A total of 336 and 4784 genes were identified as differentially expressed at 24 hpi (vs non‐infected cells) and at 48 hpi (vs. infected cells at 24 hpi), respectively, including dronc, birc5/iap5, and prp1. Functional annotation found biological processes such as cell cycle, cell growth, protein folding, and cellular amino acid metabolic processes enriched along infection.

Monitoring CAR T cell generation with a CD8-targeted lentiviral vector by single-cell transcriptomics

Abstract: [This corrects the article DOI: 10.1016/j.omtm.2021.09.019.].

Understanding the transcriptional response to ER stress in Chinese hamster ovary cells using multiplexed single cell RNA-seq

Abstract: Single cell RNA-seq (scRNA-seq) has recently been shown to provide a powerful method for the analysis of transcriptional heterogeneity in Chinese hamster ovary (CHO) cells. A potential drawback of current scRNA-seq platforms is that the cost can limit the complexity of experimental design and therefore the utility of the approach. In this manuscript, we report the use of oligonucleotide barcoding to perform multiplexed CHO cell scRNA-seq to study the impact of tunicamycin (TM), an inducer of the unfolded protein response (UPR). For this experiment, we treated a CHO-K1 GS cell line with 10μg/ml tunicamycin and acquired samples at 1, 2, 4 and 8 hr post-treatment as well as a non-treated TM-control. We transfected cells with sample-specific polyadenylated ssDNA oligonucleotide barcodes enabling us to pool all cells for scRNA-seq. The sample from which each cell originated was subsequently determined by the oligonucleotide barcode sequence. Visualisation of the transcriptome data in a reduced dimensional space confirmed that cells were not only separable by sample but were also distributed according to time post-treatment. These data were subsequently utilised to perform weighted gene co-expression analysis (WGCNA) and uncovered groups of genes associated with TM treatment. For example, the expression of one group of coexpressed genes was found to increase over the time course and were enriched for biological processes associated with ER stress. The use of multiplexed single cell RNA-seq has the potential to reduce the cost associated with higher sample numbers and avoid batch effects for future studies of CHO cell biology. Highlights Polyadenylated ssDNA oligonucleotide labelling is a viable strategy for multiplexed CHO cell scRNA-seq analysis. To demonstrate the effectiveness of the method we conducted an experiment to study the CHO cell response to tunicamycin treatment. scRNA-seq was carried out on an untreated control and at 4 time points post tunicamycin treatment. Cells from each sample were transfected with a unique oligonucleotide barcode and pooled for single cell transcriptomics. Each sample was demultiplexed post-sequencing and gene expression profiles of > 5,300 cells were obtained across the experiment. Following dimensionality reduction and visualisation, the cells were distributed according to sample identity. Analysis of the resulting data enabled improved understanding of the transcriptional response to tunicamycin treatment. Three gene coexpression modules were found to be correlated with the tunicamycin time course. Gene set enrichment analysis revealed the over representation of genes related to biological processes associated with ER stress, and protein misfolding in one of these groups of coexpressed genes. Further use of this approach will enable the CHO cell biology community to perform increasingly complex single cell experiments in a cost-effective manner.

NIH Image to ImageJ: 25 years of image analysis

Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

Fiji: an open-source platform for biological-image analysis

Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

ImageJ2: ImageJ for the next generation of scientific image data

Abstract: 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. 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. 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.

ImageJ2: ImageJ for the next generation of scientific image data

Abstract: 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. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering 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. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.