Epithelial and immune cells have long been appreciated for their contribution to the early immune response after injury; however, much less is known about the role of mesenchymal cells. Using single-nuclei RNA sequencing, we defined changes in gene expression associated with inflammation 1 day after wounding in mouse skin. Compared with those in keratinocytes and myeloid cells, we detected enriched expression of proinflammatory genes in fibroblasts associated with deeper layers of the skin. In particular, SCA1+ fibroblasts were enriched for numerous chemokines, including CCL2, CCL7, and IL-33, compared with SCA1- fibroblasts. Genetic deletion of Ccl2 in fibroblasts resulted in fewer wound-bed macrophages and monocytes during injury-induced inflammation, with reduced revascularization and re-epithelialization during the proliferation phase of healing. These findings highlight the important contribution of fibroblast-derived factors to injury-induced inflammation and the impact of immune cell dysregulation on subsequent tissue repair.

Background: Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results: To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAF_RNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions: SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

Imbalanced expression of somatic alleles in cancer can suggest functional and selective features, and can therefore indicate possible driving potential of the underlying genetic variants. To explore the correlation between allele frequency of somatic variants and total gene expression of their harboring gene, we used the unique data set of matched tumor and normal RNA and DNA sequencing data of 5523 distinct single nucleotide variants in 381 individuals across 10 cancer types obtained from The Cancer Genome Atlas (TCGA). We analyzed the allele frequency in the context of the variant and gene functional features and linked it with changes in the total gene expression. We documented higher allele frequency of somatic variants in cancer-implicated genes (Cancer Gene Census, CGC). Furthermore, somatic alleles bearing premature terminating variants (PTVs), when positioned in CGC genes, appeared to be less frequently degraded via nonsense-mediated mRNA decay, indicating possible favoring of truncated proteins by the tumor transcriptome. Among the genes with multiple PTVs with high allele frequency, ARID1, TP53 and NSD1 were known key cancer genes. All together, our analyses suggest that high allele frequency of tumor somatic variants can indicate driving functionality and can serve to identify potential cancer-implicated genes.

Prostate cancer cell lines from diverse backgrounds are important to addressing disparities in prostate cancer incidence and mortality rates among Black men. ACRJ-PC28 was developed from a transrectal needle biopsy and established via inactivation of the CDKN2A locus and simultaneous expression of human telomerase. Characterization assays included growth curve analysis, immunoblots, IHC, three-dimensional cultures, immunofluorescence imaging, confocal microscopy, flow cytometry, whole-genome sequencing (WGS), and RNA sequencing (RNA-seq). ACRJ-PC28 has been passaged more than 40 times in vitro over 10 months with a doubling time of 45 hours. Short tandem repeat profiling confirmed the novelty and human origin of the cell line. RNA-seq confirmed the expression of prostate specific genes alpha-methylacyl-CoA racemase (AMACR) and NKX3.1 and neuroendocrine specific markers synaptophysin and enolase 2 (ENO2) and IHC confirmed the presence of AMACR. Immunoblots indicated the cell line is of basal-luminal type; expresses p53 and pRB and is androgen receptor (AR) negative. WGS confirmed the absence of exonic mutations and the presence of intronic variants that appear to not affect function of AR, p53, and pRB. RNA-seq data revealed numerous TP53 and RB1 mRNA splice variants and the lack of AR mRNA expression. This is consistent with retention of p53 function in response to DNA damage and pRB function in response to contact inhibition. Soft agar anchorage-independent analysis indicated that the cells are transformed, confirmed by principal component analysis where ACRJ-PC28 cells cluster alongside other prostate cancer tumor tissues, yet was distinct. The novel methodology described should advance prostate cell line development, addressing the disparity in prostate cancer among Black men.Significance: Cell line development continues to attract less than 10% success rate. More than 98% of prostate cancer cell lines are from White men. This may contribute to the poorer response by Black men with prostate cancer to therapy compared with White men with prostate cancer, increasing overall survivorship among White men. The methodology described here to develop ACRJ-PC28, should advance the presence of Black prostate cancer cell lines thereby addressing prostate cancer disparity.

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAF_RNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAF_RNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.