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Single-cell RNA sequencing and binary hierarchical clustering define lung interstitial macrophage heterogeneity in response to hypoxia.

ABSTRACT: Few studies have examined lung interstitial macrophage (IM) molecular phenotypes after being exposed to hypoxia in vivo at the single-cell level, even though macrophages contribute to hypoxic pulmonary hypertension (PH). We aimed to determine IM diversity and its association with hypoxia-induced PH. We hypothesized that integrating single-cell RNA sequencing (scRNAseq) and binary hierarchal clustering (BHC) could resolve IM heterogeneity under normal homeostatic conditions and changes induced by hypoxia exposure. Cx3cr1^GFP/+ reporter mice were exposed to normoxic conditions (∼21% [Formula: see text]) or exposed to 1 day (D1) or 7 days (D7) of hypoxia (∼10% [Formula: see text]). We used flow cytometry to isolate Cx3cr1⁺ IMs and the 10X Genomics platform for scRNAseq, Cell Ranger, Seurat, ClusterMap, monocle, ingenuity pathway analysis, and Fisher's exact test (q value < 0.05) for functional investigations. n = 374 (normoxia), n = 2,526 (D1), and n = 1,211 (D7) IMs were included in the analyses. We identified three normoxia-related cell types, five hypoxia-associated cell types that emerged at D1, and three that appeared at D7. We describe the existence of a putative resident trained innate IM, which is present in normoxia, transiently depleted at D1, and recovered after 7 days of sustained hypoxia. We also define a rare putative pathogenic population associated with transcripts implicated in PH development that emerges at D7. In closing, we describe the successful integration of BHC with scRNAseq to determine IM heterogeneity and its association with PH. These results shed light on how resident-trained innate IMs become more heterogeneous but ultimately accustomed to hypoxia.

SUBMITTER: Campbell NV

PROVIDER: S-EPMC9273277 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Sample- and gene-based hierarchical cluster analyses have been widely adopted as tools for exploring gene expression data in high-throughput experiments. Gene expression values (read counts) generated by RNA sequencing technology (RNA-seq) are discrete variables with special statistical properties, such as over-dispersion and right-skewness. Additionally, read counts are subject to technology artifacts as differences in sequencing depth. This possesses a challenge to finding distance measures suitable for hierarchical clustering. Normalization and transformation procedures have been proposed to favor the use of Euclidean and correlation based distances. Additionally, novel model-based dissimilarities that account for RNA-seq data characteristics have also been proposed. Adequacy of dissimilarity measures has been assessed using parametric simulations or exemplar datasets that may limit the scope of the conclusions. Here, we propose the simulation of realistic conditions through creation of plasmode datasets, to assess the adequacy of dissimilarity measures for sample-based hierarchical clustering of RNA-seq data. Consistent results were obtained using plasmode datasets based on RNA-seq experiments conducted under widely different conditions. Dissimilarity measures based on Euclidean distance that only considered data normalization or data standardization were not reliable to represent the expected hierarchical structure. Conversely, using either a Poisson-based dissimilarity or a rank correlation based dissimilarity or an appropriate data transformation, resulted in dendrograms that resemble the expected hierarchical structure. Plasmode datasets can be generated for a wide range of scenarios upon which dissimilarity measures can be evaluated for sample-based hierarchical clustering analysis. We showed different ways of generating such plasmodes and applied them to the problem of selecting a suitable dissimilarity measure. We report several measures that are satisfactory and the choice of a particular measure may rely on the availability on the software pipeline of preference.

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Single-cell RNA sequencing and binary hierarchical clustering define lung interstitial macrophage heterogeneity in response to hypoxia.

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