CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues

Stoltzfus, Caleb; Filipek, Jakub; Gern, Benjamin H.; Olin, Brandy E.; Leal, Joseph M.; Lyons-Cohen, Miranda R; Huang, Jessica; Paz-Stoltzfus, Clarissa L.; Plumlee, Courtney R.; Pöschinger, Thomas; Urdahl, Kevin B.; Perro, Mario; Gerner, Michael Y.

doi:10.1101/769877

Cited by 6 publications

(6 citation statements)

References 63 publications

(121 reference statements)

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“…Several of these populations displayed distinct regional distributions within the sample. For example, lymphocytes and neutrophils skewed towards the marginal region while macrophage populations were more frequently in the interior, matching observations made in a subcutaneous colorectal cancer tumor model using whole-volume imaging and tissue clearing 15 (Fig. 4D) Sub-sampling only the monocyte/macrophage cluster (Fig.…”

Section: Mapping Immune Cell Differentiation In Relation To Position Within Tumorssupporting

confidence: 79%

ZipSeq: barcoding for real-time mapping of single cell transcriptomes

et al. 2020

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Spatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called 'ZipSeq', that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

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Section: Mapping Immune Cell Differentiation In Relation To Position Within Tumorssupporting

confidence: 79%

ZipSeq: barcoding for real-time mapping of single cell transcriptomes

et al. 2020

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“…Cell biology applications include cell classification based on nuclear and other specific markers or spatial analyses, similar to what we and other described as tissue cytometry using fluorescence or histochemical markers. 12,13,16,31,32 Many efforts are also focused on cell segmentation, automated detection and counting. 33 Digital pathology relies on histology staining and has experienced exciting development in cell segmentation and classification.…”

Section: Discussionmentioning

confidence: 99%

“…[12][13][14] With optical sectioning microscopy, querying cell types based on specific markers could also be expanded to 3dimensional (3D) space, increasing the ability to assay cellular structure and tissue architecture. 13,15,16 Multiplex imaging based on cell markers may offer a path to classify cells in tissues, especially with the availability of tissue cytometry software tools that facilitate the analytical process. For example, we recently described the Volumetric Tissue Exploration and Analysis (VTEA) tool which enables the semi-automated classification of labeled cells in 3D image volumes.…”

Section: Introductionmentioning

confidence: 99%

In situclassification of cell types in human kidney tissue using 3D nuclear staining

Woloshuk

Khochare

Almulhim

et al. 2020

Preprint

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To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers.Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.

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“…To understand why there was a reduction in the number of sigmoid trajectory differentiation events, we applied a modified version of the histo-cytometry technique (STAR Methods) (Stoltzfus et al, 2020). We imaged entire LGs expressing eater-dsRed and dome-MESO-GFP and performed automated image analysis to determine the relative amounts of these markers in individual cells in the…”

Section: Infection Changes Cell Differentiation Patterns In the Lgmentioning

confidence: 99%

“…To perform in vivo analysis on LGs, a method of histo-cytometry (Stoltzfus et al, 2020) was applied to extract fluorescent signals of dome-MESO-GFP and eater-dsRed and the positional information of individual cells from the LGs of wild-type control and E.coli infected larvae. Three main steps of histo-cytometry were performed: 1) Image tissue in 3D: Entire LGs of genotype dome-MESO-GFP; eater-dsRed were imaged by a FV1000 microscopy with a step size 1.5 μm using exactly equivalent laser settings across wild-type control and infection groups.…”

Section: In Vivo Analysis Of Lgsmentioning

confidence: 99%

Kinetics of blood cell differentiation during hematopoiesis revealed by quantitative long-term live imaging

Carr

Dvoskin

et al. 2022

Preprint

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Stem cells typically reside in a specialized physical and biochemical environment that facilitates regulation of their behavior. For this reason, stem cells are ideally studied in contexts that maintain this precisely constructed microenvironment while still allowing for live imaging. Here, we describe a long-term organ culture and imaging strategy for hematopoiesis in flies that takes advantage of powerful genetic and transgenic tools available in this system. We find that fly blood progenitors undergo symmetric cell divisions and that their division is both linked to cell size and is spatially oriented. Using quantitative imaging to simultaneously track markers for stemness and differentiation in progenitors, we identify two types of differentiation that exhibit distinct kinetics. Moreover, we find that infection-induced activation of hematopoiesis occurs through modulation of the kinetics of cell differentiation. Overall, our results show that even subtle shifts in proliferation and differentiation kinetics can have large and aggregate effects to transform blood progenitors from a quiescent to an activated state.

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CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues

Cited by 6 publications

References 63 publications

ZipSeq: barcoding for real-time mapping of single cell transcriptomes

ZipSeq: barcoding for real-time mapping of single cell transcriptomes

In situclassification of cell types in human kidney tissue using 3D nuclear staining

Kinetics of blood cell differentiation during hematopoiesis revealed by quantitative long-term live imaging

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