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Andersson, N.

Publications and source records attributed to Andersson, N..

2 recordsLinked to original sources

Multiplexed single-cell transcriptomic analysis of normal and impaired lung development in the mouse

During late lung development alveolar and microvascular development is finalized to enable sufficient gas exchange. Impaired late lung development manifests as bronchopulmonary dysplasia (BPD) in preterm infants. Single-cell RNA sequencing (scRNA-seq) allows for assessment of complex cellular dynamics during biological processes, such as development. Here, we use MULTI-seq to generate scRNA-seq profiles of over 66,000 cells from 36 mice during normal or impaired lung development secondary to hyperoxia. We observed dynamic populations of cells, including several rare cell types and putative progenitors. Hyperoxia exposure, which mimics the BPD phenotype, alters the composition of all cellular compartments, particularly alveolar epithelium, capillary endothelium and macrophage populations. We identified several BPD-associated signatures, including Pdgfra in fibroblasts, Activin A in capillary endothelial cells, and Csf1-Csf1r and Ccl2-Ccr2 signaling in macrophages and neutrophils. Our data provides a novel single-cell view of cellular changes associated with late lung development in health and in disease.

cell biology

PRISM: Recovering cell type specific expression profilesfrom composite RNA-seq data

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

bioinformatics