Cell segmentation-free inference of cell types from in situ transcriptomics data

Park, Jeongbin; Choi, Wonyl; Tiesmeyer, Sebastian; Long, Brian; Borm, Lars E.; Garren, Emma; Nguyen, Thuc Nghi; Tasic, Bosiljka; Codeluppi, Simone; Graf, Tobias; Schlesner, Matthias; Stegle, Oliver; Eils, Roland; Ishaque, Naveed

doi:10.1038/s41467-021-23807-4

Cited by 77 publications

(81 citation statements)

References 47 publications

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“…A number of methods have been developed to use subcellular gene expression patterns to circumvent cell segmentation, which can be challenging. For example, SSAM assigns cell type labels directly to pixels without cell segmentation (Park et al 2021). stLearn uses a similar approach but further clusters spatially proximal pixels that are assigned to the same cell type (Pham et al 2020).…”

Section: Subcellular Structure Analysismentioning

confidence: 99%

Advances in spatial transcriptomic data analysis

et al. 2021

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Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

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Section: Subcellular Structure Analysismentioning

confidence: 99%

Advances in spatial transcriptomic data analysis

et al. 2021

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“…SSAM (Park et al, 2021) was defined to identify tissue niches in transcriptomics data. The first step is to create probability maps of the object types.…”

Section: Spot-based Spatial Cell-type Analysis By Multidimensional Mrna Density Estimation (Ssam)mentioning

confidence: 99%

Spatial Statistics for Understanding Tissue Organization

2022

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Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.

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“…Although immunostaining is easy and can even distinguish among astrocyte subtypes, some of these proteins are also expressed by other cell types such as oligodendrocytes (S100b) and neurons (GLT1, GLAST), whereas GFAP is undetectable in many hippocampal astrocytes [ 121 ]. Alternatively, detection of RNAs by in situ hybridization, in situ sequencing [ 122 , 123 ], smFISH [ 124 , 125 ], seqFISH [ 126 ], and osmFISH [ 127 , 128 ] can be used to identify astrocytes even if antibodies are unavailable for the protein product. However, RNA detection often cannot reveal cell morphology.…”

Section: Microscopic Imaging Of Astrocyte Developmentmentioning

confidence: 99%

Potential of Multiscale Astrocyte Imaging for Revealing Mechanisms Underlying Neurodevelopmental Disorders

Kumamoto

Tsurugizawa

2021

IJMS

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Astrocytes provide trophic and metabolic support to neurons and modulate circuit formation during development. In addition, astrocytes help maintain neuronal homeostasis through neurovascular coupling, blood–brain barrier maintenance, clearance of metabolites and nonfunctional proteins via the glymphatic system, extracellular potassium buffering, and regulation of synaptic activity. Thus, astrocyte dysfunction may contribute to a myriad of neurological disorders. Indeed, astrocyte dysfunction during development has been implicated in Rett disease, Alexander’s disease, epilepsy, and autism, among other disorders. Numerous disease model mice have been established to investigate these diseases, but important preclinical findings on etiology and pathophysiology have not translated into clinical interventions. A multidisciplinary approach is required to elucidate the mechanism of these diseases because astrocyte dysfunction can result in altered neuronal connectivity, morphology, and activity. Recent progress in neuroimaging techniques has enabled noninvasive investigations of brain structure and function at multiple spatiotemporal scales, and these technologies are expected to facilitate the translation of preclinical findings to clinical studies and ultimately to clinical trials. Here, we review recent progress on astrocyte contributions to neurodevelopmental and neuropsychiatric disorders revealed using novel imaging techniques, from microscopy scale to mesoscopic scale.

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Cell segmentation-free inference of cell types from in situ transcriptomics data

Cited by 77 publications

References 47 publications

Advances in spatial transcriptomic data analysis

Advances in spatial transcriptomic data analysis

Spatial Statistics for Understanding Tissue Organization

Potential of Multiscale Astrocyte Imaging for Revealing Mechanisms Underlying Neurodevelopmental Disorders

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