Identification and Characterization of Cellular Heterogeneity within Human Late Developmental Stage Dissociated Lung by CITE‐Seq
Authors: Bandyopadhyay Gautam , Lillis Jacquelyn , Misra Ravi S , Myers Jason R , Ashton John M , Huyck Heidie L , Krenitsky Daria , Romas Stephen T , Poole Cory J , Holden‐Wiltse Jeanne , Katzman Philip J , Deutsch Gail , Bhattacharya Soumyaroop , Mariani Thomas J , Pryhuber Gloria S 
Published: June 2021

A developmental pattern of lung cell maturation known to occur in late gestation and postnatal life remains poorly understood in humans. This study demonstrates sub‐population enriched, as well as single cell, transcriptome analyses with and without surface protein cell‐indexing, to assess cell specific expression in developing lungs obtained from neonatal and pediatric donors.

Methods

We isolated four major lung cell types (epithelial [EPI], endothelial [END] and non‐endothelial mesenchymal [MES] cells and leukocytes [MIC]) using fluorescence‐activated cell sorting from donor lungs. Next‐generation sequencing libraries were prepared from sorted cell RNA. Similarly, transcriptional profiles of 5500 newborn human lung cells were generated using single cell RNA sequencing (sc‐RNAseq) and CITE‐seq techniques. Cell ranger v2.2.0 and CITE‐seq‐Count (v1.3.1a) were used to process transcriptional and surface protein data for each cell. Downstream analysis was performed within Seurat (v2.3) and the R 3.5.0 environment. Low quality cells were removed including if >12.5% mitochondrial transcription or < 500 UMI/cell. Hyper variable genes were detected and cells were visualized using a tSNE plot. Clusters were identified and annotated based on cell‐type cluster specific marker genes associated with the Lung GENS database (Thorax. 2015; 70:1092).

Results

Bulk RNAseq confirmed developmental cell‐specific patterns of gene expression. On single cells, overlaying surface protein quantification onto cell type annotated clusters in tSNE space shows good RNA and protein signature agreement. For example, CD31 marks an endothelial cluster with probable substructure based on differential gene expression of VWF, HPGD, and CDH5. Likewise, EpCAM was restricted to a tight cluster consistent with lung EPIs, a portion of which co‐expressed podoplanin, consistent with type I alveolar EPIs. In addition, CD45+ marked leukocyte clusters. Defining a cell as CD45+ if surface protein quantification for CD45 was >2, created a subset of 1,138 cells further delineated by RNA into those consistent with NK T, B and T cells and a second larger myeloid cluster. Based on co‐expression of genes used to identify pulmonary macrophages, CD11b(ITGAM), CD206(MRC1), CD169(SIGLEC1) and CD15(FUT4), 10% of the myeloid cluster were consistent with monocytes (FUT4− ITGAM+MRC1−SIGLEC−), 4% interstitial macrophages (FUT4−ITGAM+MRC1+ SIGLEC−) and 0.01% alveolar macrophages (FUT4−ITGAM+MRC1+SIGLEC+).

Conclusions

We have demonstrated surface marker indexing of neonatal human lung cell subpopulations, consistent with non‐biased single cell transcriptome content, identifying differentiated antigen presenting cells and great potential to finely identify further cell subsets in early postnatal lung.

Support or Funding Information

Funding: NHLBI U01HL122700

This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.