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	<title>bioRxiv Channel: Allen Institute for Cell Science</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "Allen Institute for Cell Science"
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	<item rdf:about="https://biorxiv.org/cgi/content/short/123042v1?rss=1">
<title>
<![CDATA[
Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/123042v1?rss=1"
</link>
<description><![CDATA[
We present a CRISPR/Cas9 genome editing strategy to systematically tag endogenous proteins with fluorescent tags in human inducible pluripotent stem cells. To date we have generated multiple human iPSC lines with GFP tags for 10 proteins representing key cellular structures. The tagged proteins include alpha tubulin, beta actin, desmoplakin, fibrillarin, lamin B1, non-muscle myosin heavy chain IIB, paxillin, Sec61 beta, tight junction protein ZO1, and Tom20. Our genome editing methodology using Cas9 ribonuclear protein electroporation and fluorescence-based enrichment of edited cells resulted in <0.1-24% HDR across all experiments. Clones were generated from each edited population and screened for precise editing. [~]25% of the clones contained precise mono-allelic edits at the targeted locus. Furthermore, 92% (36/39) of expanded clonal lines satisfied key quality control criteria including genomic stability, appropriate expression and localization of the tagged protein, and pluripotency. Final clonal lines corresponding to each of the 10 cellular structures are now available to the research community. The data described here, including our editing protocol, genetic screening, quality control assays, and imaging observations, can serve as an initial resource for genome editing in cell biology and stem cell research.
]]></description>
<dc:creator>Roberts, B.</dc:creator>
<dc:creator>Haupt, A.</dc:creator>
<dc:creator>Tucker, A.</dc:creator>
<dc:creator>Grancharova, T.</dc:creator>
<dc:creator>Arakaki, J.</dc:creator>
<dc:creator>Fuqua, M. A.</dc:creator>
<dc:creator>Nelson, A.</dc:creator>
<dc:creator>Hookway, C.</dc:creator>
<dc:creator>Ludmann, S. A.</dc:creator>
<dc:creator>Mueller, I. M.</dc:creator>
<dc:creator>Yang, R.</dc:creator>
<dc:creator>Horwitz, A. R.</dc:creator>
<dc:creator>Rafelski, S. M.</dc:creator>
<dc:creator>Gunawardane, R. N.</dc:creator>
<dc:date>2017-03-31</dc:date>
<dc:identifier>doi:10.1101/123042</dc:identifier>
<dc:title><![CDATA[Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/216606v1?rss=1">
<title>
<![CDATA[
Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/216606v1?rss=1"
</link>
<description><![CDATA[
Fluorescence microscopy has enabled imaging of key subcellular structures in living cells; however, the use of fluorescent dyes and proteins is often expensive, time-consuming, and damaging to cells. Here, we present a tool for the prediction of fluorescently labeled structures in live cells solely from 3D brightfield microscopy images. We show the utility of this approach in predicting several structures of interest from the same static 3D brightfield image, and show that the same tool can prospectively be used to predict the spatiotemporal position of these structures from a bright-field time series. This approach could also be useful in a variety of application areas, such as cross-modal image registration, quantification of live cell imaging, and determination of cell state changes.
]]></description>
<dc:creator>Ounkomol, C.</dc:creator>
<dc:creator>Fernandes, D. A.</dc:creator>
<dc:creator>Seshamani, S.</dc:creator>
<dc:creator>Maleckar, M. M.</dc:creator>
<dc:creator>Collman, F.</dc:creator>
<dc:creator>Johnson, G. R.</dc:creator>
<dc:date>2017-11-09</dc:date>
<dc:identifier>doi:10.1101/216606</dc:identifier>
<dc:title><![CDATA[Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/238378v1?rss=1">
<title>
<![CDATA[
Building a 3D Integrated Cell 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/238378v1?rss=1"
</link>
<description><![CDATA[
We present a conditional generative model for learning variation in cell and nuclear morphology and predicting the location of subcellular structures from 3D microscopy images. The model generalizes well to a wide array of structures and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of the approach by producing and evaluating photo-realistic 3D cell images using the generative model, and show that the conditional nature of the model provides the ability to predict the localization of unobserved structures, given cell and nuclear morphology. We additionally explore the models utility in a number of applications, including cellular integration from multiple experiments and exploration of variation in structure localization. Finally, we discuss the model in the context of foundational and contemporary work and suggest forthcoming extensions.
]]></description>
<dc:creator>Johnson, G. R.</dc:creator>
<dc:creator>Donovan-Maiye, R. M.</dc:creator>
<dc:creator>Maleckar, M. M.</dc:creator>
<dc:date>2017-12-21</dc:date>
<dc:identifier>doi:10.1101/238378</dc:identifier>
<dc:title><![CDATA[Building a 3D Integrated Cell]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/289504v1?rss=1">
<title>
<![CDATA[
Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/289504v1?rss=1"
</link>
<description><![CDATA[
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.
]]></description>
<dc:creator>Ounkomol, C.</dc:creator>
<dc:creator>Seshamani, S.</dc:creator>
<dc:creator>Maleckar, M. M.</dc:creator>
<dc:creator>Collman, F.</dc:creator>
<dc:creator>Johnson, G.</dc:creator>
<dc:date>2018-03-27</dc:date>
<dc:identifier>doi:10.1101/289504</dc:identifier>
<dc:title><![CDATA[Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/342881v1?rss=1">
<title>
<![CDATA[
Scarless gene tagging of transcriptionally silent genes in hiPSCs to visualize cardiomyocyte sarcomeres in live cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/342881v1?rss=1"
</link>
<description><![CDATA[
We describe a multi-step CRISPR/Cas9 gene editing method to create endogenously tagged GFP-fusions of transcriptionally silent genes in human induced pluripotent stem cells (hiPSCs), allowing visualization of proteins that are only expressed upon differentiation. To do this, we designed a donor template containing the monomeric EGFP (mEGFP) fusion tag and an mCherry selection cassette delivered in tandem to a target locus via homology directed repair (HDR). mCherry expression was driven by a constitutive promoter and served as a drug-free, excisable selection marker. Following selection, the mCherry cassette was excised with Cas9, creating an mEGFP-fusion with the target gene. We achieved scarless excision by using repetitive sequences to guide microhomology-mediated end joining (MMEJ) and introduce linker sequences between the mEGFP tag and the target gene. Using this strategy, we successfully tagged genes encoding the cardiomyocyte sarcomeric proteins troponin I (TNNI1), alpha-actinin (ACTN2), titin (TTN), myosin light chain 2a (MYL7), and myosin light chain 2v (MYL2) with mEGFP in undifferentiated hiPSCs. This methodology provides a general strategy for scarlessly introducing tags to transcriptionally silent loci in hiPSCs.
]]></description>
<dc:creator>Roberts, B.</dc:creator>
<dc:creator>Arakaki, J.</dc:creator>
<dc:creator>Gerbin, K. A.</dc:creator>
<dc:creator>Malik, H.</dc:creator>
<dc:creator>Nelson, A.</dc:creator>
<dc:creator>Hendershott, M. C.</dc:creator>
<dc:creator>Hookway, C.</dc:creator>
<dc:creator>Ludmann, S. A.</dc:creator>
<dc:creator>Mueller, I. A.</dc:creator>
<dc:creator>Yang, R.</dc:creator>
<dc:creator>Rafelski, S. M.</dc:creator>
<dc:creator>Gunawardane, R. N.</dc:creator>
<dc:date>2018-06-09</dc:date>
<dc:identifier>doi:10.1101/342881</dc:identifier>
<dc:title><![CDATA[Scarless gene tagging of transcriptionally silent genes in hiPSCs to visualize cardiomyocyte sarcomeres in live cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-06-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.18.423371v1?rss=1">
<title>
<![CDATA[
Automated hiPSC culture and sample preparation for 3D live cell microscopy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.18.423371v1?rss=1"
</link>
<description><![CDATA[
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.
]]></description>
<dc:creator>Coston, M. E.</dc:creator>
<dc:creator>Gregor, B. W.</dc:creator>
<dc:creator>Arakaki, J.</dc:creator>
<dc:creator>Borensztejn, A.</dc:creator>
<dc:creator>Do, T. P.</dc:creator>
<dc:creator>Fuqua, M. A.</dc:creator>
<dc:creator>Haupt, A.</dc:creator>
<dc:creator>Hendershott, M. C.</dc:creator>
<dc:creator>Leung, W.</dc:creator>
<dc:creator>Mueller, I. A.</dc:creator>
<dc:creator>Nelson, A. M.</dc:creator>
<dc:creator>Rafelski, S. M.</dc:creator>
<dc:creator>Swain-Bowden, M. J.</dc:creator>
<dc:creator>Tang, W. J.</dc:creator>
<dc:creator>Thirstrup, D. J.</dc:creator>
<dc:creator>Wiegraebe, W.</dc:creator>
<dc:creator>Yan, C.</dc:creator>
<dc:creator>Gunawardane, R. N.</dc:creator>
<dc:creator>Gaudreault, N.</dc:creator>
<dc:date>2020-12-19</dc:date>
<dc:identifier>doi:10.1101/2020.12.18.423371</dc:identifier>
<dc:title><![CDATA[Automated hiPSC culture and sample preparation for 3D live cell microscopy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/491035v1?rss=1">
<title>
<![CDATA[
The Allen Cell Structure Segmenter: a new open sourcetoolkit for segmenting 3D intracellular structures influorescence microscopy images 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/491035v1?rss=1"
</link>
<description><![CDATA[
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.
]]></description>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Ding, L.</dc:creator>
<dc:creator>Viana, M. P.</dc:creator>
<dc:creator>Hendershott, M. C.</dc:creator>
<dc:creator>Yang, R.</dc:creator>
<dc:creator>Mueller, I. A.</dc:creator>
<dc:creator>Rafelski, S. M.</dc:creator>
<dc:date>2018-12-08</dc:date>
<dc:identifier>doi:10.1101/491035</dc:identifier>
<dc:title><![CDATA[The Allen Cell Structure Segmenter: a new open sourcetoolkit for segmenting 3D intracellular structures influorescence microscopy images]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-12-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.08.415562v1?rss=1">
<title>
<![CDATA[
Robust integrated intracellular organization of the human iPS cell: where, how much, and how variable? 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.08.415562v1?rss=1"
</link>
<description><![CDATA[
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 structures 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.
]]></description>
<dc:creator>Viana, M. P.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Knijnenburg, T. A.</dc:creator>
<dc:creator>Vasan, R.</dc:creator>
<dc:creator>Yan, C.</dc:creator>
<dc:creator>Arakaki, J. E.</dc:creator>
<dc:creator>Bailey, M.</dc:creator>
<dc:creator>Berry, B.</dc:creator>
<dc:creator>Borensztejn, A.</dc:creator>
<dc:creator>Brown, J. M.</dc:creator>
<dc:creator>Carlson, S.</dc:creator>
<dc:creator>Cass, J. A.</dc:creator>
<dc:creator>Chaudhuri, B.</dc:creator>
<dc:creator>Cordes Metzler, K. R.</dc:creator>
<dc:creator>Coston, M. E.</dc:creator>
<dc:creator>Crabtree, Z. J.</dc:creator>
<dc:creator>Davidson, S.</dc:creator>
<dc:creator>DeLizo, C. M.</dc:creator>
<dc:creator>Dhaka, S.</dc:creator>
<dc:creator>Dinh, S. Q.</dc:creator>
<dc:creator>Do, T. P.</dc:creator>
<dc:creator>Domingus, J.</dc:creator>
<dc:creator>Donovan-Maiye, R. M.</dc:creator>
<dc:creator>Foster, T. J.</dc:creator>
<dc:creator>Frick, C. L.</dc:creator>
<dc:creator>Fujioka, G.</dc:creator>
<dc:creator>Fuqua, M. A.</dc:creator>
<dc:creator>Gehring, J. L.</dc:creator>
<dc:creator>Gerbin, K. A.</dc:creator>
<dc:creator>Grancharova, T.</dc:creator>
<dc:creator>Gregor, B. W.</dc:creator>
<dc:creator>Harrylock, L.</dc:creator>
<dc:creator>Haupt, A.</dc:creator>
<dc:creator>Hendershott, M. C.</dc:creator>
<dc:creator>Hookway, C.</dc:creator>
<dc:creator>Horwitz, A. R.</dc:creator>
<dc:creator>Hughes, C.</dc:creator>
<dc:creator>Isaac, E. J.</dc:creator>
<dc:creator>Johnson, G. R.</dc:creator>
<dc:creator>Kim, B.</dc:creator>
<dc:creator>Leonard, A. N.</dc:creator>
<dc:creator>Leung, W.</dc:creator>
<dc:date>2020-12-10</dc:date>
<dc:identifier>doi:10.1101/2020.12.08.415562</dc:identifier>
<dc:title><![CDATA[Robust integrated intracellular organization of the human iPS cell: where, how much, and how variable?]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.05.26.081083v1?rss=1">
<title>
<![CDATA[
Cell states beyond transcriptomics: integrating structural organization and gene expression in hiPSC-derived cardiomyocytes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.05.26.081083v1?rss=1"
</link>
<description><![CDATA[
We present a quantitative co-analysis of RNA abundance and sarcomere organization in single cells and an integrated framework to predict subcellular organization states from gene expression. We used human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes expressing mEGFP-tagged alpha-actinin-2 to develop quantitative image analysis tools for systematic and automated classification of subcellular organization. This captured a wide range of sarcomeric organization states within cell populations that were previously difficult to quantify. We performed RNA FISH targeting genes identified by single cell RNA sequencing to simultaneously assess the relationship between transcript abundance and structural states in single cells. Co-analysis of gene expression and sarcomeric patterns in the same cells revealed biologically meaningful correlations that could be used to predict organizational states. This study establishes a framework for multi-dimensional analysis of single cells to study the relationships between gene expression and subcellular organization and to develop a more nuanced description of cell states.

Graphical AbstractTranscriptional profiling and structural classification was performed on human induced pluripotent stem cell-derived cardiomyocytes to characterize the relationship between transcript abundance and subcellular organization.



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/081083v1_ufig1.gif" ALT="Figure 1">
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]]></description>
<dc:creator>Gerbin, K. A.</dc:creator>
<dc:creator>Grancharova, T.</dc:creator>
<dc:creator>Donovan-Maiye, R.</dc:creator>
<dc:creator>Hendershott, M. C.</dc:creator>
<dc:creator>Brown, J.</dc:creator>
<dc:creator>Dinh, S. Q.</dc:creator>
<dc:creator>Gehring, J. L.</dc:creator>
<dc:creator>Hirano, M.</dc:creator>
<dc:creator>Johnson, G. R.</dc:creator>
<dc:creator>Nath, A.</dc:creator>
<dc:creator>Nelson, A.</dc:creator>
<dc:creator>Roco, C. M.</dc:creator>
<dc:creator>Rosenberg, A. B.</dc:creator>
<dc:creator>Sluzewski, M. F.</dc:creator>
<dc:creator>Viana, M. P.</dc:creator>
<dc:creator>Yan, C.</dc:creator>
<dc:creator>Zaunbrecher, R. J.</dc:creator>
<dc:creator>Cordes Metzler, K. R.</dc:creator>
<dc:creator>Menon, V.</dc:creator>
<dc:creator>Palecek, S. P.</dc:creator>
<dc:creator>Seelig, G.</dc:creator>
<dc:creator>Gaudreault, N.</dc:creator>
<dc:creator>Knijnenburg, T.</dc:creator>
<dc:creator>Rafelski, S. M.</dc:creator>
<dc:creator>Theriot, J. A.</dc:creator>
<dc:creator>Gunawardane, R. N.</dc:creator>
<dc:date>2020-05-27</dc:date>
<dc:identifier>doi:10.1101/2020.05.26.081083</dc:identifier>
<dc:title><![CDATA[Cell states beyond transcriptomics: integrating structural organization and gene expression in hiPSC-derived cardiomyocytes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-05-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.22.441027v1?rss=1">
<title>
<![CDATA[
A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.22.441027v1?rss=1"
</link>
<description><![CDATA[
We performed a comprehensive analysis of the transcriptional changes within and across cell populations during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using the single cell RNA-seq combinatorial barcoding method SPLiT-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. As expected, 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 differentiation population composition and 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.
]]></description>
<dc:creator>Grancharova, T.</dc:creator>
<dc:creator>Gerbin, K. A.</dc:creator>
<dc:creator>Rosenberg, A. B.</dc:creator>
<dc:creator>Roco, C. M.</dc:creator>
<dc:creator>Arakaki, J.</dc:creator>
<dc:creator>DeLizzo, C.</dc:creator>
<dc:creator>Dinh, S. Q.</dc:creator>
<dc:creator>Donovan-Maiye, R.</dc:creator>
<dc:creator>Hirano, M.</dc:creator>
<dc:creator>Nelson, A.</dc:creator>
<dc:creator>Tang, J.</dc:creator>
<dc:creator>Theriot, J. A.</dc:creator>
<dc:creator>Yan, C.</dc:creator>
<dc:creator>Menon, V.</dc:creator>
<dc:creator>Palecek, S. P.</dc:creator>
<dc:creator>Seelig, G.</dc:creator>
<dc:creator>Gunawardane, R. N.</dc:creator>
<dc:date>2021-04-23</dc:date>
<dc:identifier>doi:10.1101/2021.04.22.441027</dc:identifier>
<dc:title><![CDATA[A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.25.441198v1?rss=1">
<title>
<![CDATA[
Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.25.441198v1?rss=1"
</link>
<description><![CDATA[
1 -Digital light microscopy provides powerful tools for quantitatively probing the real-time dynamics of subcellular structures. While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable information and scientific knowledge which can be shared for further analysis (value). Keeping notes on microscopy experiments and quality control procedures ought to be straightforward, as the microscope is a machine whose components are defined and the performance measurable. Nevertheless, to this date, no universally adopted community-driven specifications exist that delineate the required information about the microscope hardware and acquisition settings (i.e., microscopy "data provenance" metadata) and the minimally accepted calibration metrics (i.e., microscopy quality control metadata) that should be automatically recorded by both commercial microscope manufacturers and customized microscope developers. In the absence of agreed guidelines, it is inherently difficult for scientists to create comprehensive records of imaging experiments and ensure the quality of resulting image data or for manufacturers to incorporate standardized reporting and performance metrics. To add to the confusion, microscopy experiments vary greatly in aim and complexity, ranging from purely descriptive work to complex, quantitative and even sub-resolution studies that require more detailed reporting and quality control measures.

To solve this problem, the 4D Nucleome Initiative (4DN) (1, 2) Imaging Standards Working Group (IWG), working in conjunction with the BioImaging North America (BINA) Quality Control and Data Management Working Group (QC-DM-WG) (3), here propose light Microscopy Metadata specifications that scale with experimental intent and with the complexity of the instrumentation and analytical requirements. They consist of a revision of the Core of the Open Microscopy Environment (OME) Data Model, which forms the basis for the widely adopted Bio-Formats library (4-6), accompanied by a suite of three extensions, each with three tiers, allowing the classification of imaging experiments into levels of increasing imaging and analytical complexity (7, 8). Hence these specifications not only provide an OME-based comprehensive set of metadata elements that should be recorded, but they also specify which subset of the full list should be recorded for a given experimental tier. In order to evaluate the extent of community interest, an extensive outreach effort was conducted to present the proposed metadata specifications to members of several core-facilities and international bioimaging initiatives including the European Light Microscopy Initiative (ELMI), Global BioImaging (GBI), and European Molecular Biology Laboratory (EMBL) - European Bioinformatics Institute (EBI). Consequently, close ties were established between our endeavour and the undertakings of the recently established QUAlity Assessment and REProducibility for Instruments and Images in Light Microscopy global community initiative (9). As a result this flexible 4DN-BINA-OME (NBO namespace) framework (7, 8) represents a turning point towards achieving community-driven Microscopy Metadata standards that will increase data fidelity, improve repeatability and reproducibility, ease future analysis and facilitate the verifiable comparison of different datasets, experimental setups, and assays, and it demonstrates the method for future extensions. Such universally accepted microscopy standards would serve a similar purpose as the Encode guidelines successfully adopted by the genomic community (10, 11). The intention of this proposal is therefore to encourage participation, critiques and contributions from the entire imaging community and all stakeholders, including research and imaging scientists, facility personnel, instrument manufacturers, software developers, standards organizations, scientific publishers, and funders.
]]></description>
<dc:creator>Hammer, M.</dc:creator>
<dc:creator>Huisman, M.</dc:creator>
<dc:creator>Rigano, A.</dc:creator>
<dc:creator>Boehm, U.</dc:creator>
<dc:creator>Chambers, J. J.</dc:creator>
<dc:creator>Gaudreault, N.</dc:creator>
<dc:creator>North, A. J.</dc:creator>
<dc:creator>Pimentel, J. A.</dc:creator>
<dc:creator>Sudar, D.</dc:creator>
<dc:creator>Bajcsy, P.</dc:creator>
<dc:creator>Brown, C. M.</dc:creator>
<dc:creator>Corbett, A. D.</dc:creator>
<dc:creator>Faklaris, O.</dc:creator>
<dc:creator>Lacoste, J.</dc:creator>
<dc:creator>Laude, A.</dc:creator>
<dc:creator>Nelson, G.</dc:creator>
<dc:creator>Nitschke, R.</dc:creator>
<dc:creator>Farzam, F.</dc:creator>
<dc:creator>Smith, C.</dc:creator>
<dc:creator>Grunwald, D.</dc:creator>
<dc:creator>Strambio-De-Castillia, C.</dc:creator>
<dc:date>2021-04-26</dc:date>
<dc:identifier>doi:10.1101/2021.04.25.441198</dc:identifier>
<dc:title><![CDATA[Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-26</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
