Single cell tracking based on Voronoi partition via stable matching

Chang, Young Hwan; Linsley, Jeremy W.; Lamstein, Josh; Kalra, Jaslin; Epstein, Irina; Mariya,; Daily, Kenneth; Synder, Phil; Omberg, Larsson; Heiser, Laura M.; Finkbeiner, Steve

doi:10.1109/cdc42340.2020.9304436

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Using a combination of GradCAM and pixel ablation simulations, we show that the predictions of live and dead are sourced from different subcellular compartments such as the plasma membrane and the nucleus. Altered plasma membrane and retracted neurites are prominent features in images of neuronal morphology that are well known to correlate with vitality (55) and are likely easily appreciated by human curators. However, EGFP morphology signal in the region of the nucleus has not been previously appreciated as an indicator of vitality.…”

Section: Discussionmentioning

confidence: 99%

“…The resulting black and white image is further processed so that each cell is identified as an independent object with its own numerical identifier. Single-cell tracking was performed using a custom Voronoi tracking algorithm that monitors the movements of regionalized binary objects through space (55). Survival analysis was performed by defining the last time point alive as the time point before the GEDI ratio of a longitudinally imaged neuron exceeds the empirically calculated GEDI threshold for death, the last time point a human curator tracking cells finds a neuron alive, or the time point before the GEDI-CNN classifies a neuron as dead.…”

Section: Automated Imaging and Image Processing Pipelinementioning

confidence: 99%

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Superhuman cell death detection with biomarker-optimized neural networks

Linsley

Lamstein

et al. 2021

Sci. Adv.

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Section: Discussionmentioning

confidence: 99%

Section: Automated Imaging and Image Processing Pipelinementioning

confidence: 99%

Superhuman cell death detection with biomarker-optimized neural networks

Linsley

Lamstein

et al. 2021

Sci. Adv.

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“…5A and fig. S5) (58,59). Subtypes of cortical interneurons are reportedly vulnerable to T21 dosage (60), but increased cell death had been reported only in heterogenous T21 neuronal populations to date (17,31,33,54,61,62).…”

Section: T21 Gene Dosage Effects In Mature Neural Cell Typesmentioning

confidence: 99%

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction

Bansal,

Banda,

Glatt-Deeley

et al. 2024

Sci. Adv.

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Excess gene dosage from chromosome 21 (chr21) causes Down syndrome (DS), spanning developmental and acute phenotypes in terminal cell types. Which phenotypes remain amenable to intervention after development is unknown. To address this question in a model of DS neurogenesis, we derived trisomy 21 (T21) human induced pluripotent stem cells (iPSCs) alongside, otherwise, isogenic euploid controls from mosaic DS fibroblasts and equipped one chr21 copy with an inducible XIST transgene. Monoallelic chr21 silencing by XIST is near-complete and irreversible in iPSCs. Differential expression reveals that T21 neural lineages and iPSCs share suppressed translation and mitochondrial pathways and activate cellular stress responses. When XIST is induced before the neural progenitor stage, T21 dosage correction suppresses a pronounced skew toward astrogenesis in neural differentiation. Because our transgene remains inducible in postmitotic T21 neurons and astrocytes, we demonstrate that XIST efficiently represses genes even after terminal differentiation, which will empower exploration of cell type–specific T21 phenotypes that remain responsive to chr21 dosage.

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“…However, current multi-omic integration strategies are not compatible with spatial assays, as they fail to include spatial information and incorrectly assume homogeneity within the population, particularly when dealing with serial sections. Alternatively, cell tracking algorithms 36 , designed to track cell movements across video frames, can be adapted to treat serial images as sequential frames but do not leverage the rich molecular profiles provided by MTI. Therefore, it is essential to develop a single-cell integration method that combines molecular characteristics with spatial context to effectively integrate multimodal or multi-panel datasets obtained from serial tissue sections.…”

Section: Introductionmentioning

confidence: 99%

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Heussner,

Watson,

Eddy

et al. 2024

Preprint

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Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial tissue sectioning to comprehensively profile samples by imaging each section with unique biomarker panels or assays. The dependence on serial sections is attributed to technological limitations of MTI panel size or incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due to inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. To address this problem, we first use Monte-Carlo simulations to estimate the overlap between serial 5μm-thick sections. We then introduce COEXIST, a novel algorithm that synergistically combines shared molecular profiles with spatial information to seamlessly integrate serial sections at the single-cell level. We demonstrate COEXIST necessity and performance across several applications. These include combining MTI panels for improved spatial single-cell profiling, rectification of miscalled cell phenotypes using a single MTI panel, and the comparison of MTI platforms at single-cell resolution. COEXIST not only elevates MTI platform validation but also overcomes the constraints of MTI's panel size and the limitation of full nuclei on a single slide, capturing more intact nuclei in consecutive sections and thus enabling deeper profiling of cell lineages and functional states.

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Single cell tracking based on Voronoi partition via stable matching

Cited by 4 publications

References 27 publications

Superhuman cell death detection with biomarker-optimized neural networks

Superhuman cell death detection with biomarker-optimized neural networks

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

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