Cell Culture Differentiation and Proliferation Conditions Influence the In Vitro Regeneration of the Human Airway Epithelium.

The human airway mucociliary epithelium can be recapitulated in vitro using primary cells cultured in an Air-Liquid Interface (ALI), a reliable surrogate to perform pathophysiological studies. As tremendous variations exist between media used for ALI-cultured human airway epithelial cells, our study aimed to evaluate the impact of several media (BEGMTM, PneumaCultTM, "Half&Half" and "Clancy") on cell type distribution using single-cell RNA sequencing and imaging. Our work revealed the impact of these media on cell composition, gene expression profile, cell signaling and epithelial morphology. We found higher proportions of multiciliated cells in PneumaCultTM-ALI and Half&Half, stronger EGF signaling from basal cells in BEGMTM-ALI, differential expression of the SARS-CoV-2 entry factor ACE2, and distinct secretome transcripts depending on media used. We also established that proliferation in PneumaCultTM-Ex Plus favored secretory cell fate, showing the key influence of proliferation media on late differentiation epithelial characteristics. Altogether, our data offer a comprehensive repertoire for evaluating the effects of culture conditions on airway epithelial differentiation and will help to choose the most relevant medium according to the processes to be investigated such as cilia, mucus biology or viral infection. We detail useful parameters that should be explored to document airway epithelial cell fate and morphology.

Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network.

Single-cell CRISPR-based transcriptome screens are potent genetic tools for concomitantly assessing the expression profiles of cells targeted by a set of guides RNA (gRNA), and inferring target gene functions from the observed perturbations. However, due to various limitations, this approach lacks sensitivity in detecting weak perturbations and is essentially reliable when studying master regulators such as transcription factors. To overcome the challenge of detecting subtle gRNA induced transcriptomic perturbations and classifying the most responsive cells, we developed a new supervised autoencoder neural network method. Our Sparse supervised autoencoder (SSAE) neural network provides selection of both relevant features (genes) and actual perturbed cells. We applied this method on an in-house single-cell CRISPR-interference-based (CRISPRi) transcriptome screening (CROP-Seq) focusing on a subset of long non-coding RNAs (lncRNAs) regulated by hypoxia, a condition that promote tumor aggressiveness and drug resistance, in the context of lung adenocarcinoma (LUAD). The CROP-seq library of validated gRNA against a subset of lncRNAs and, as positive controls, HIF1A and HIF2A, the 2 main transcription factors of the hypoxic response, was transduced in A549 LUAD cells cultured in normoxia or exposed to hypoxic conditions during 3, 6 or 24 h. We first validated the SSAE approach on HIF1A and HIF2 by confirming the specific effect of their knock-down during the temporal switch of the hypoxic response. Next, the SSAE method was able to detect stable short hypoxia-dependent transcriptomic signatures induced by the knock-down of some lncRNAs candidates, outperforming previously published machine learning approaches. This proof of concept demonstrates the relevance of the SSAE approach for deciphering weak perturbations in single-cell transcriptomic data readout as part of CRISPR-based screening.

Neutrophil extracellular traps formed during chemotherapy confer treatment resistance via TGF-ß activation.

Metastasis is the major cause of cancer death, and the development of therapy resistance is common. The tumor microenvironment can confer chemotherapy resistance (chemoresistance), but little is known about how specific host cells influence therapy outcome. We show that chemotherapy induces neutrophil recruitment and neutrophil extracellular trap (NET) formation, which reduces therapy response in mouse models of breast cancer lung metastasis. We reveal that chemotherapy-treated cancer cells secrete IL-1ß, which in turn triggers NET formation. Two NET-associated proteins are required to induce chemoresistance: integrin-αvß1, which traps latent TGF-ß, and matrix metalloproteinase 9, which cleaves and activates the trapped latent TGF-ß. TGF-ß activation causes cancer cells to undergo epithelial-to-mesenchymal transition and correlates with chemoresistance. Our work demonstrates that NETs regulate the activities of neighboring cells by trapping and activating cytokines and suggests that chemoresistance in the metastatic setting can be reduced or prevented by targeting the IL-1ß-NET-TGF-ß axis.