CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Hodneland, Erlend; Kögel, Tanja; Frei, Dominik; Gerdes, Hans‐Hermann; Lundervold, Arvid

doi:10.1186/1751-0473-8-16

Cited by 89 publications

(86 citation statements)

References 25 publications

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“…Mouse nuclei segmentation: Processing and segmentation of mouse nuclei is performed using the MATLAB toolbox called "CellSegm" 69 . The toolbox provides the user with several input options for smoothing (coherence enhancing diffusion, edge enhancing diffusion, gaussian) and thresholding (iterative thresholding, adaptive thresholding, gradient thresholding, ridge enhancement).…”

Section: Generation Of Reference Emission Spectral Profilesmentioning

confidence: 99%

dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

Maynard

Tippani

Takahashi

et al. 2019

Preprint

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ABSTRACTMultiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.

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Section: Generation Of Reference Emission Spectral Profilesmentioning

confidence: 99%

dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

Maynard

Tippani

Takahashi

et al. 2019

Preprint

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“…It has taken the community many decades and great endeavor to segment, identify, and analyze cells for explaining the cellular and molecular processes of health and disease. Studies have showed that cell segmentation can contribute to the better cell recognition in various fields of cell biology (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and great advances have been achieved. However, it still has a long way to go before meeting the ultimate goal that the developed method can segment and identify different types of cells autonomously and the biologists can trust the segmentation and identification results blindly.…”

Section: The Future Of Cell Segmentationmentioning

confidence: 99%

“…However, most state‐of‐the‐art methods or software tools mainly rely on existing basic image processing algorithms developed many decades ago. For example, the watershed algorithm is adopted by Cellsegm , SMASH , ImageJ , CellProfiler , Alanazi's method , and the recently reported method by Tsujikawa et al (CYTOA 95:4; pp 389–398). Classical thresholding is adopted by Cellsegm, SMASH, ImageJ, and Alanazi's method while K ‐means clustering is adopted by Tsujikawa's method.…”

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confidence: 99%

Cell Segmentation for Image Cytometry: Advances, Insufficiencies, and Challenges

Wang

2018

Cytometry Pt A

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“…Recently developed multiplexed fluorescence in-situ hybridization (mFISH) (Chen et al, 2015;Codeluppi et al, 2018;Lubeck et al, 2014) and in situ mRNA tissue sequencing (Ke et al, 2013;Lee et al, 2015;Maniatis et al, 2019;Ståhl et al, 2016;Vickovic et al, 2019a;Wang et al, 2018) techniques have enabled the simultaneous measurement of multiple mRNAs in a spatial context. Application of cell segmentation algorithms to images obtained by these techniques identifies cells, and allows classification of classes or types of cells together with their locations (Hodneland et al, 2013;Jiang et al, 2019;Kong et al, 2015;Salvi et al, 2019). The rapid increase in establishment of in situ transcriptomics platforms inspired the inception of the SpaceTx Consortium (Perkel, 2019), which aims to systematically evaluate these platforms and protocols.…”

Section: Introductionmentioning

confidence: 99%

Cell segmentation-free inference of cell types fromin situtranscriptomics data

Park

Choi

Tiesmeyer

et al. 2019

Preprint

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Multiplexed fluorescence in situ hybridization techniques have enabled cell class or type identification by mRNA quantification in situ. However, inaccurate cell segmentation can result in incomplete cell-type and tissue characterization. Here, we present a robust segmentation-free computational framework, applicable to a variety of in situ transcriptomics platforms, called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM). SSAM assumes that spatial distribution of mRNAs relates to organization of higher complexity structures (e.g. cells or tissue layers) and performs de novo cell-type and tissue domain identification. Optionally, SSAM can also integrate prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. SSAM outperforms segmentation-based results, demonstrating that segmentation of cells is not required for inferring cell-type signatures, cell-type organization or tissue domains. KeywordsIn situ transcriptomics, spatial cell-type calling, segmentation-free, multiplexed FISH, SSAM, osmFISH, mFISH, multiplexed smFISH, MERFISH, spatially resolved RNA profiling Lignell et al., 2017), or total poly-A RNA (Codeluppi et al., 2018;Moffitt et al., 2018).Accurate cell segmentation, however, is difficult to achieve due to tightly apposed or overlapping cells, uneven cell borders, varying cell and nuclear shapes, signal intensity variation, probe fluorescence emission efficiency variation, and tiling artifacts (Thomas and John, 2017). The underlying problem is that the cellular structures one would want to segment are much smaller than the resolution of a diffraction-limited microscope. Therefore, there is a need for robust segmentation-independent methods for identification of cell-type signatures, cell-type organization, and tissue domains from multidimensional mRNA expression data in complex tissues. These methods could be used for datasets lacking landmarks or to validate segmentation-based approaches and identify associated artifacts.Here we introduce a novel computational framework named Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a multi-platform segmentation-free computational framework for identifying cell-type signatures and reconstructing cell-type and tissue domain maps from both 2D-and 3D-spatially resolved in situ transcriptomics data. We apply SSAM to three mouse brain tissue images obtained by different techniques: the somatosensory cortex by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We demonstrate the performance of SSAM in identifying 1) cell types in situ, 2) spatial distribution of cell types, 3) spatial relationships between cell types, and 4) tissue domains (e.g., cortical layers) based on the local composition of cell types without having even segmented a single cell. ResultsThe SSAM computational fra...

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CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Cited by 89 publications

References 25 publications

dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

Cell Segmentation for Image Cytometry: Advances, Insufficiencies, and Challenges

Cell segmentation-free inference of cell types fromin situtranscriptomics data

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