Whole-body integration of gene expression and single-cell morphology

Vergara, Hernando; Pape, Constantin; Meechan, Kimberly; Zinchenko, Valentyna; Genoud, Christel; Wanner, Adrian; Mutemi, Kevin Nzumbi; Titze, Benjamin; Templin, Rachel; Bertucci, Paola; Simakov, Oleg; Dürichen, Wiebke; Machado, Pedro; Savage, Emily L.; Schermelleh, Lothar; Schwab, Yannick; Friedrich, Rainer W.; Kreshuk, Anna; Tischer, Christian; Arendt, Detlev

doi:10.1016/j.cell.2021.07.017

Cited by 78 publications

(87 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…bioformats2raw can be used for writing OME-NGFF from standalone Java applications and omero-cli-zarr is available for exporting from OMERO 6 . Reading is implemented in ome-zarr-py, which has been integrated into the napari viewer 7 , in Fiji via the MoBIE plugin 8 and finally via Viv-based vizarr for access in the browser 9 . Permissively licensed example datasets from the Image Data Resource (IDR) 10 have been converted into Zarr and stored in an S3-object storage bucket for public consumption (Extended Data Fig.…”

Section: Next-generation File Formatsmentioning

confidence: 99%

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies

et al. 2021

Self Cite

View full text Add to dashboard Cite

The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.

show abstract

Section: Next-generation File Formatsmentioning

confidence: 99%

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Integrating them can help in understanding whether changes in gene expression have a direct consequence not only on how individual cells look, but also on how they interact with their neighbours. However, the resolution of these different data types may be significantly different, making a direct correspondence between modalities hard to achieve (Vergara et al, 2021). In these situations, modality alignment becomes paramount (Lopez et al, 2019).…”

Section: Multimodal Learningmentioning

confidence: 99%

Deep learning for bioimage analysis in developmental biology

et al. 2021

View full text Add to dashboard Cite

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

show abstract

“…The BigWarp ( Hildebrand et al, 2017 ) and elmr ( Zheng et al, 2018 ) software tools, which are now extended into the natverse platform ( Bates et al, 2019 ), enable the integration of EM or CLEM data into public data repositories of other light level template drosophila or fish larval brains. Beyond the correlation among different imaging modalities, the information content of ultrastructural data can be enriched by registration onto spatial gene expression atlases ( Vergara et al, 2021 ).…”

Section: Registration Strategiesmentioning

confidence: 99%

“…These advances were driven by the need to upscale the amount of ultrastructural data that can be obtained. Developing commensurate analysis approaches to deal with the emerging large data volumes, such as “random forest” algorithms for segmentation, remain at the frontier of connectomics research ( Berning et al, 2015 ; Januszewski et al, 2018 ; Scheffer, 2018 ; Schubert et al, 2019 ; Dorkenwald et al, 2020 ; Turner et al, 2020 ; Vergara et al, 2021 ). In comparison, tailored small-scale volume SEM of specific regions of interest resemble a “Niwaki” (Japanese for “sculpting trees”) task aimed at precision rather than high throughput.…”

Section: Introduction and Scopementioning

confidence: 99%

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

et al. 2021

View full text Add to dashboard Cite

Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a “needle-in-the-haystack” problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are “one-shot” imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, “multi-shot” approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects.

show abstract

Whole-body integration of gene expression and single-cell morphology

Cited by 78 publications

References 108 publications

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies

Deep learning for bioimage analysis in developmental biology

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

Contact Info

Product

Resources

About