Diseases leading to lung structural and functional disruption pose a major threat to human health (Tobin, 2005; Nathan et al., 2019; Podolanczuk et al., 2021). Mouse models represent a common choice to investigate physiological and pathological processes (Cheon and Orsulic, 2011; Benam et al., 2015; Walsh et al., 2017). To date, researches conducted on mouse lungs have been instrumental in dissecting developmental processes as well as pulmonary diseases, for example, studies on tumor-infiltrating myeloid cells in lung cancer (Zilionis et al., 2019), generation of pulmonary fibrosis (Wu et al., 2020), neonatal development (Scaffa et al., 2021) and injury repair (Leist et al., 2020). The recent development of novel state-of-the-art genomic technologies allows a deeper exploration of the lung functionally and structurally. A growing number of mouse-lung genomic datasets have been generated and have been paramount for illustrating fundamental transcriptomic changes at the tissue level (Angelidis et al., 2019; Vila Ellis et al., 2020) or revealing heterogeneity in resident lung cells (Angelidis et al., 2019; Gillich et al., 2020; Travaglini et al., 2020; Hurskainen et al., 2021) via bulk and single-cell RNA sequencing (scRNA-seq). However, due to technical limitations of bulk and scRNA-seq, irreversible elimination of topological information hinders further analysis focusing on temporal-spatial dimensions (Lahnemann et al., 2020). In this regard, spatial transcriptome (ST) approaches, rising as the annual technology of Nature Methods in 2020 (Marx, 2021), can help to fill the knowledge gap between structure and function (Stahl et al., 2016; Lein et al., 2017; Moor and Itzkovitz, 2017). In this technique, spatio-temporal expression of genes is revealed by sequencing the mRNA captured in situ, with spatial coordinates labeled and translated (Close et al., 2021; Larsson et al., 2021; Zhuang, 2021). Using ST, researchers investigated themechanisms of lung diseases and development at new latitudes, which is crucial for understanding the regulation of cell fate decisions and transitions of lung cells (Saviano et al., 2020; Liao et al., 2021). ST was first applied to mouse lung models by Boyd et al., in 2020, who reported the lethal immunopathology of the damage-responsive lung fibroblasts during severe influenza virus infection (Boyd et al., 2020). Furthermore, Xu found the location changes of T helper cells after immunization by integrating ST and scRNA-seq (Xu et al., 2021). Besides, STwas also employed to explore themechanism that promotes lung tissue remodeling following respiratory viral infection (Beppu et al., 2021). The studies mentioned above have highlighted the importance of ST, in combination with multiple cutting-edge technologies, to gain basic and previously inaccessible information about lung biology and pathophysiology. However, most recent lung-related studies using ST are limited by a low spatial resolution (~55–60 μm) and a restricted field of view (42.25mm2), making the discrimination of different cells within the same capture spot challenging (Asp et al., 2020; Waylen et al., 2020). In addition, it is difficult to provide a detailed description of the subtle structures in the lung, such as bronchi, blood vessels, etc. Consequently, ST data of lungs with a resolution of approaching cellular-to-sub-cellular level are highly desired (Longo et al., 2021; Rao et al., 2021; Zhang et al., 2021). Traditionally, mouse lung development can be subdivided into five main stages based on morphological changes: Embryonic (E9.5–E11.5), Pseudoglandular (E11.5–E16.5), Canalicular (E16.5–E17.5), Saccular (E17.5–P5) and Alveolar (postnatal (P) 5–P30), followed by maturation (P30 - adult) (Mujahid et al., 2013; Chao et al., 2015). Upon entering the maturation period, the basic structures of the mouse lung have developed and subsequently increase in size as the mouse grows (Schittny, 2017). It has been reported that the proximal lung matures more rapidly than the distal lung (Rawlins et al., 2007; Mund et al., 2008; Beauchemin et al., 2016; He et al., 2022). However, there is no research revealing the significant markers related to the spatial asynchronous development in the early maturation of mouse lung. Therefore, we selected P35 mouse lungs for spatiotemporal transcriptomic experiments to observe the spatial distribution of mature tissue cells within the mouse lung and dissect the spatial asynchronous development. For this study, we employed Stereo-seq (Chen et al., 2021), a novel ST approach with a 500 nmresolution that allows us to precisely detect mouse lungs’ structures and investigate the spatiotemporal transcriptomic change during lung development. Bioinformatic analysis on our dataset suggested the differences in the expression of several genes from proximal to distal lungs. As far as we know, it is the first time to demonstrate such alternation via spatial gene mapping. In addition, our dataset provided strong support for further studies on the lung’s tissue structure and functional exercise and explored lung diseases’ mechanisms and treatment options. And the Stereo-seq data can be accessed and explored interactively on the website: https://db.cngb.org/stomics/datasets/STDS0000062?tab=explore
Spatial Transcriptome Uncovers the Mouse Lung Architectures and Functions
Volpe G.Investigation
;
2022-01-01
Abstract
Diseases leading to lung structural and functional disruption pose a major threat to human health (Tobin, 2005; Nathan et al., 2019; Podolanczuk et al., 2021). Mouse models represent a common choice to investigate physiological and pathological processes (Cheon and Orsulic, 2011; Benam et al., 2015; Walsh et al., 2017). To date, researches conducted on mouse lungs have been instrumental in dissecting developmental processes as well as pulmonary diseases, for example, studies on tumor-infiltrating myeloid cells in lung cancer (Zilionis et al., 2019), generation of pulmonary fibrosis (Wu et al., 2020), neonatal development (Scaffa et al., 2021) and injury repair (Leist et al., 2020). The recent development of novel state-of-the-art genomic technologies allows a deeper exploration of the lung functionally and structurally. A growing number of mouse-lung genomic datasets have been generated and have been paramount for illustrating fundamental transcriptomic changes at the tissue level (Angelidis et al., 2019; Vila Ellis et al., 2020) or revealing heterogeneity in resident lung cells (Angelidis et al., 2019; Gillich et al., 2020; Travaglini et al., 2020; Hurskainen et al., 2021) via bulk and single-cell RNA sequencing (scRNA-seq). However, due to technical limitations of bulk and scRNA-seq, irreversible elimination of topological information hinders further analysis focusing on temporal-spatial dimensions (Lahnemann et al., 2020). In this regard, spatial transcriptome (ST) approaches, rising as the annual technology of Nature Methods in 2020 (Marx, 2021), can help to fill the knowledge gap between structure and function (Stahl et al., 2016; Lein et al., 2017; Moor and Itzkovitz, 2017). In this technique, spatio-temporal expression of genes is revealed by sequencing the mRNA captured in situ, with spatial coordinates labeled and translated (Close et al., 2021; Larsson et al., 2021; Zhuang, 2021). Using ST, researchers investigated themechanisms of lung diseases and development at new latitudes, which is crucial for understanding the regulation of cell fate decisions and transitions of lung cells (Saviano et al., 2020; Liao et al., 2021). ST was first applied to mouse lung models by Boyd et al., in 2020, who reported the lethal immunopathology of the damage-responsive lung fibroblasts during severe influenza virus infection (Boyd et al., 2020). Furthermore, Xu found the location changes of T helper cells after immunization by integrating ST and scRNA-seq (Xu et al., 2021). Besides, STwas also employed to explore themechanism that promotes lung tissue remodeling following respiratory viral infection (Beppu et al., 2021). The studies mentioned above have highlighted the importance of ST, in combination with multiple cutting-edge technologies, to gain basic and previously inaccessible information about lung biology and pathophysiology. However, most recent lung-related studies using ST are limited by a low spatial resolution (~55–60 μm) and a restricted field of view (42.25mm2), making the discrimination of different cells within the same capture spot challenging (Asp et al., 2020; Waylen et al., 2020). In addition, it is difficult to provide a detailed description of the subtle structures in the lung, such as bronchi, blood vessels, etc. Consequently, ST data of lungs with a resolution of approaching cellular-to-sub-cellular level are highly desired (Longo et al., 2021; Rao et al., 2021; Zhang et al., 2021). Traditionally, mouse lung development can be subdivided into five main stages based on morphological changes: Embryonic (E9.5–E11.5), Pseudoglandular (E11.5–E16.5), Canalicular (E16.5–E17.5), Saccular (E17.5–P5) and Alveolar (postnatal (P) 5–P30), followed by maturation (P30 - adult) (Mujahid et al., 2013; Chao et al., 2015). Upon entering the maturation period, the basic structures of the mouse lung have developed and subsequently increase in size as the mouse grows (Schittny, 2017). It has been reported that the proximal lung matures more rapidly than the distal lung (Rawlins et al., 2007; Mund et al., 2008; Beauchemin et al., 2016; He et al., 2022). However, there is no research revealing the significant markers related to the spatial asynchronous development in the early maturation of mouse lung. Therefore, we selected P35 mouse lungs for spatiotemporal transcriptomic experiments to observe the spatial distribution of mature tissue cells within the mouse lung and dissect the spatial asynchronous development. For this study, we employed Stereo-seq (Chen et al., 2021), a novel ST approach with a 500 nmresolution that allows us to precisely detect mouse lungs’ structures and investigate the spatiotemporal transcriptomic change during lung development. Bioinformatic analysis on our dataset suggested the differences in the expression of several genes from proximal to distal lungs. As far as we know, it is the first time to demonstrate such alternation via spatial gene mapping. In addition, our dataset provided strong support for further studies on the lung’s tissue structure and functional exercise and explored lung diseases’ mechanisms and treatment options. And the Stereo-seq data can be accessed and explored interactively on the website: https://db.cngb.org/stomics/datasets/STDS0000062?tab=exploreI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
