Single-cell Expression Analysis Research Articles

Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue.

Studies on human parathyroids are generally limited to hyperfunctioning glands owing to the difficulty in obtaining normal human tissue. We therefore obtained non-human primate (NHP) parathyroids to provide a suitable alternative for sequencing that would bear a close semblance to human organs. Single-cell RNA expression analysis of parathyroids from four healthy adult M. mulatta reveals a continuous trajectory of epithelial cell states. Pseudotime analysis based on transcriptomic signatures suggests a progression from GCM2 hi progenitors to mature parathyroid hormone (PTH)-expressing epithelial cells with increasing core mitochondrial transcript abundance along pseudotime. We sequenced, as a comparator, four histologically characterized hyperfunctioning human parathyroids with varying oxyphil and chief cell abundance and leveraged advanced computational techniques to highlight similarities and differences from non-human primate parathyroid expression dynamics. Predicted cell-cell communication analysis reveals abundant endothelial cell interactions in the parathyroid cell microenvironment in both human and NHP parathyroid glands. We show abundant RARRES2 transcripts in both human adenoma and normal primate parathyroid cells and use coimmunostaining to reveal high levels of RARRES2 protein (also known as chemerin) in PTH-expressing cells, which could indicate that RARRES2 plays an unrecognized role in parathyroid endocrine function. The data obtained are the first single-cell RNA transcriptome to characterize nondiseased parathyroid cell signatures and to show a transcriptomic progression of cell states within normal parathyroid glands, which can be used to better understand parathyroid cell biology.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Single cell and bulk RNA expression analyses identify enhanced hexosamine biosynthetic pathway and O-GlcNAcylation in acute myeloid leukemia blasts and stem cells.

Acute myeloid leukemia (AML) is the most common acute leukemia in adults with an overall poor prognosis and high relapse rate. Multiple factors including genetic abnormalities, differentiation defects and altered cellular metabolism contribute to AML development and progression. Though the roles of oxidative phosphorylation and glycolysis are defined in AML, the role of the hexosamine biosynthetic pathway (HBP), which regulates the O-GlcNAcylation of cytoplasmic and nuclear proteins, remains poorly defined. We studied the expression of the key enzymes involved in the HBP in AML blasts and stem cells by RNA sequencing at the single-cell and bulk level. We performed flow cytometry to study OGT protein expression and global O-GlcNAcylation. We studied the functional effects of inhibiting O-GlcNAcylation on transcriptional activation in AML cells by Western blotting and real time PCR and on cell cycle by flow cytometry. We found higher expression levels of the key enzymes in the HBP in AML as compared to healthy donors in whole blood. We observed elevated O-GlcNAc Transferase (OGT) and O-GlcNAcase (OGA) expression in AML stem and bulk cells as compared to normal hematopoietic stem and progenitor cells (HSPCs). We also found that both AML bulk cells and stem cells show significantly enhanced OGT protein expression and global O-GlcNAcylation as compared to normal HSPCs, validating our in silico findings. Gene set analysis showed substantial enrichment of the NF-κB pathway in AML cells expressing high OGT levels. Inhibition of O-GlcNAcylation decreased NF-κB nuclear translocation and the expression of selected NF-κB-dependent genes controlling cell cycle. It also blocked cell cycle progression suggesting a link between enhanced O-GlcNAcylation and NF-κB activation in AML cell survival and proliferation. Our study suggests the HBP may prove a potential target, alone or in combination with other therapeutic approaches, to impact both AML blasts and stem cells. Moreover, as insufficient targeting of AML stem cells by traditional chemotherapy is thought to lead to relapse, blocking HBP and O-GlcNAcylation in AML stem cells may represent a novel promising target to control relapse.

Abstract 6430: Gene expression signature quantifies the cellular contribution of the tumor vs. the microenvironment in determining colorectal cancer prognosis and therapeutic outcome

Abstract Accumulating evidence has suggested that cancer progression and therapeutic response may depend on not only tumor cells but also their tumor microenvironment (TME). However, their contributory roles are complex and not well understood. Here we report an analysis of 2373 human colorectal cancer (CRC) tumors representing all stages, leading to identification of a 20-gene signature capable of quantifying the cellular contribution of the tumor vs. the TME in determining colorectal cancer prognosis and therapeutic outcome. We classified these tumors into the consensus molecular subtypes (CMS1-4) with distinct, variable TME cellular features defined by CIBERSORT deconvolution analysis of bulk gene expression data. Surprisingly, all the 10 positive signature genes (portending worse survival) were strongly correlated with the TME-rich CMS1 and CMS4 tumors. By contrast, all the 10 negative signature genes (portending better survival) were highly related to the TME-poor CMS2 and CMS3 tumors. A single cell expression analysis from an independent database further revealed that strikingly, all the 10 positive genes were principally expressed by the immune/stromal TME whereas all the 10 negative genes were predominantly expressed in epithelial tumor cells. These data suggest that the TME has an equally important role as do tumor cells in determining CRC prognosis and support targeting the TME to improve survival. Moreover, distinct gene expression from tumor cells versus the TME also showed a differential impact on therapeutic outcome. Retrospective analyses on two independent clinical trial datasets show that the 20-gene signature score significantly predicted progression free survival in metastatic CRC patients treated with cetuximab, an FDA-approved EGFR inhibitor therapy. These data suggest a potential for the 20-gene signature as a predictive biomarker to identify “sensitive” versus “resistant” subpopulations of CRC patients to improve outcome of EGFR-targeted therapy. Citation Format: Mingli Yang, Timothy J. Yeatman, Michael V. Nebozhyn, Michael J. Schell, Lance Pflieger, Andrey Loboda, Warren J. Pledger, Ramani Soundararajan, Michelle Maurin, Heiman Wang, Jetsen Rodriguez Silva, Ashley Alden. Gene expression signature quantifies the cellular contribution of the tumor vs. the microenvironment in determining colorectal cancer prognosis and therapeutic outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6430.

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.

The SGLT2I Dapagliflozin reverses metabolic stress driving myocardial inflammation in a mouse model of heart failure with preserved ejection fraction: insights from single-cell RNA sequencing

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is often associated with metabolic distress and represents a therapeutic challenge. Metabolism-induced systemic inflammation links comorbidities with HFpEF. How metabolic changes impact myocardial inflammation in the context of HFpEF is not known. Methods We generated a model of metabolic HFpEF by feeding ApoE knockout (ApoE KO) mice a high-fat diet (HFD), and used single-cell expression analysis (scRNA-seq) on CD45+ cardiac cells to evaluate the involvement of inflammation. By focusing our analysis on macrophages, we obtained high-resolution identification of subsets of these cells in the heart, enabling us to study the outcomes of metabolic distress on the cardiac macrophage infiltrate and identify a macrophage-to-cardiomyocyte (CMC) regulatory axis. To test whether a clinically relevant sodium glucose cotransporter-2 inhibitor (SGLT2i) could ameliorate the cardiac immune infiltrate profile in our model, ApoE KO-HDF mice were randomized to receive the SGLT2i dapagliflozin (DAPA) or vehicle for 8 weeks. Results We describe a new metabolic HFpEF model: mice present with reduced diastolic function, reduced exercise tolerance, and increased pulmonary congestion associated with cardiac lipid overload and reduced polyunsaturated fatty acids. The main immune cell types infiltrating the heart included 4 sub-populations of resident and monocyte-derived macrophages displaying a pro-inflammatory profile exclusively in ApoE KO-HFD mice. Lipid overload had a direct effect on inflammatory gene activation in macrophages, mediated through endoplasmic-reticulum stress pathways. Investigation of the macrophage-to-CMC regulatory axis revealed the potential effects on CMCs of multiple inflammatory cytokines secreted by macrophages, impacting pathways such as hypertrophy, fibrosis, and autophagy. Finally, we describe the anti-inflammatory effect of SGLT2i. Conclusions Through a newly described model of HFpEF based on metabolic stress, and using scRNA-seq, we have determined the effects of metabolic distress on cardiac inflammatory cells, in particular on macrophages, and suggest SGLT2i as potential therapeutic agents for the targeting of a specific phenotype of HFpEF.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.

Comprehensive analysis of KLF2 as a prognostic biomarker associated with fibrosis and immune infiltration in advanced hepatocellular carcinoma

PurposeMost Hepatocellular carcinoma (HCC) patients are in advanced or metastatic stage at the time of diagnosis. Prognosis for advanced HCC patients is dismal. This study was based on our previous microarray results, and aimed to explore the promising diagnostic and prognostic markers for advanced HCC by focusing on the important function of KLF2.MethodsThe Cancer Genome Atlas (TCGA), Cancer Genome Consortium database (ICGC), and the Gene Expression Comprehensive Database (GEO) provided the raw data of this study research. The cBioPortal platform, CeDR Atlas platform, and the Human Protein Atlas (HPA) website were applied to analyze the mutational landscape and single-cell sequencing data of KLF2. Basing on the results of single-cell sequencing analyses, we further explored the molecular mechanism of KLF2 regulation in the fibrosis and immune infiltration of HCC.ResultsDecreased KLF2 expression was discovered to be mainly regulated by hypermethylation, and indicated a poor prognosis of HCC. Single-cell level expression analyses revealed KLF2 was highly expressed in immune cells and fibroblasts. The function enrichment analysis of KLF2 targets indicated the crucial association between KLF2 and tumor matrix. 33-genes related with cancer associated fibroblasts (CAFs) were collected to identify the significant association of KLF2 with fibrosis. And SPP1 was validated as a promising prognostic and diagnostic marker for advanced HCC patients. CXCR6 CD8+ T cells were noted as a predominant proportion in the immune microenvironment, and T cell receptor CD3D was discovered to be a potential therapeutic biomarker for HCC immunotherapy.ConclusionThis study identified that KLF2 is an important factor promoting HCC progression by affecting the fibrosis and immune infiltration, highlighting its great potential as a novel prognostic biomarker for advanced HCC.

Machine learning modeling and prognostic value analysis of invasion-related genes in cutaneous melanoma

In this study, we aimed to develop an invasion-related risk signature and prognostic model for personalized treatment and prognosis prediction in skin cutaneous melanoma (SKCM), as invasion plays a crucial role in this disease. We identified 124 differentially expressed invasion-associated genes (DE-IAGs) and selected 20 prognostic genes (TTYH3, NME1, ORC1, PLK1, MYO10, SPINT1, NUPR1, SERPINE2, HLA-DQB2, METTL7B, TIMP1, NOX4, DBI, ARL15, APOBEC3G, ARRB2, DRAM1, RNF213, C14orf28, and CPEB3) using Cox and LASSO regression to establish a risk score. Gene expression was validated through single-cell sequencing, protein expression, and transcriptome analysis. Negative correlations were discovered between risk score, immune score, and stromal score using ESTIMATE and CIBERSORT algorithms. High- and low-risk groups exhibited significant differences in immune cell infiltration and checkpoint molecule expression. The 20 prognostic genes effectively differentiated between SKCM and normal samples (AUCs >0.7). We identified 234 drugs targeting 6 genes from the DGIdb database. Our study provides potential biomarkers and a risk signature for personalized treatment and prognosis prediction in SKCM patients. We developed a nomogram and machine-learning prognostic model to predict 1-, 3-, and 5-year overall survival (OS) using risk signature and clinical factors. The best model, Extra Trees Classifier (AUC = 0.88), was derived from pycaret's comparison of 15 classifiers. The pipeline and app are accessible at https://github.com/EnyuY/IAGs-in-SKCM.

Notch2 deficiency generates functionally impaired cDCs and macrophages that correlates with intestinal dysbiosis and low-grade inflammation

Abstract Myeloid cell types including conventional dendritic cells (cDCs) and macrophages are key responders to innate stimuli at epithelial barriers. They are equipped with innate receptors to sense danger signals and pathogens, becoming the first line of immune defense and thereby maintaining homeostasis. The expression of selected transcription factors was shown to be essential for the development of these myeloid subsets including Batf3, Notch2, Irf4, and Irf8 among others. We performed single-cell RNA expression analysis across eight different conditional KO lines and identified multiple defects across each line that compromised the development of unique subsets and also altered expression profiles on the remaining subsets. Within CD11c expressing cells, Notch2 deficiency in particular, seems to impair not only type 2 dendritic cells (cDC2) but also macrophage subsets across several tissues. Mice defective for Notch2 were shown to be susceptible to C. rodentium infection. We observed that the lack of Notch2 was accompanied by intestinal dysbiosis, which produced low-grade inflammation. The altered microbiome could be transferred to WT mice by co-housing, which suggests that the lack of either subset enabled the growth of an opportunistic microorganism that could prevail in competitive settings. Among the dysregulated innate pathways, we identified one that appears to correlate with this dysbiosis, and its deletion was sufficient to reproduce the phenotype in preliminary experiments. Collectively, we hypothesize that Notch2 deficiency alters the functional properties of the cDCs and intestinal macrophages ultimately leading to intestinal dysbiosis and a pre-pathologic inflammatory condition. Supported by NIH intramural funding

Whole Exome Sequencing Reveals Novel Candidate Genes in Familial Forms of Glaucomatous Neurodegeneration

Glaucoma is the largest cause of irreversible blindness with a multifactorial genetic etiology. This study explores novel genes and gene networks in familial forms of primary open angle glaucoma (POAG) and primary angle closure glaucoma (PACG) to identify rare mutations with high penetrance. Thirty-one samples from nine MYOC-negative families (five POAG and four PACG) underwent whole-exome sequencing and analysis. A set of prioritized genes and variations were screened in an independent validation cohort of 1536 samples and the whole-exome data from 20 sporadic patients. The expression profiles of the candidate genes were analyzed in 17 publicly available expression datasets from ocular tissues and single cells. Rare, deleterious SNVs in AQP5, SRFBP1, CDH6 and FOXM1 from POAG families and in ACACB, RGL3 and LAMA2 from PACG families were found exclusively in glaucoma cases. AQP5, SRFBP1 and CDH6 also revealed significant altered expression in glaucoma in expression datasets. Single-cell expression analysis revealed enrichment of identified candidate genes in retinal ganglion cells and corneal epithelial cells in POAG; whereas for PACG families, retinal ganglion cells and Schwalbe's Line showed enriched expression. Through an unbiased exome-wide search followed by validation, we identified novel candidate genes for familial cases of POAG and PACG. The SRFBP1 gene found in a POAG family is located within the GLC1M locus on Chr5q. Pathway analysis of candidate genes revealed enrichment of extracellular matrix organization in both POAG and PACG.

The gold-ringed octopus (Amphioctopus fangsiao) genome and cerebral single-nucleus transcriptomes provide insights into the evolution of karyotype and neural novelties

BackgroundColeoid cephalopods have distinctive neural and morphological characteristics compared to other invertebrates. Early studies reported massive genomic rearrangements occurred before the split of octopus and squid lineages (Proc Natl Acad Sci U S A 116:3030-5, 2019), which might be related to the neural innovations of their brain, yet the details remain elusive. Here we combine genomic and single-nucleus transcriptome analyses to investigate the octopod chromosome evolution and cerebral characteristics.ResultsWe present a chromosome-level genome assembly of a gold-ringed octopus, Amphioctopus fangsiao, and a single-nucleus transcriptome of its supra-esophageal brain. Chromosome-level synteny analyses estimate that the chromosomes of the ancestral octopods experienced multiple chromosome fission/fusion and loss/gain events by comparing with the nautilus genome as outgroup, and that a conserved genome organization was detected during the evolutionary process from the last common octopod ancestor to their descendants. Besides, protocadherin, GPCR, and C2H2 ZNF genes are thought to be highly related to the neural innovations in cephalopods (Nature 524:220–4, 2015), and the chromosome analyses pinpointed several collinear modes of these genes on the octopod chromosomes, such as the collinearity between PCDH and C2H2 ZNF, as well as between GPCR and C2H2 ZNF. Phylogenetic analyses show that the expansion of the octopod protocadherin genes is driven by a tandem-duplication mechanism on one single chromosome, including two separate expansions at 65 million years ago (Ma) and 8–14 Ma, respectively. Furthermore, we identify eight cell types (i.e., cholinergic and glutamatergic neurons) in the supra-esophageal brain of A. fangsiao, and the single-cell expression analyses reveal the co-expression of protocadherin and GPCR in specific neural cells, which may contribute to the neural development and signal transductions in the octopod brain.ConclusionsThe octopod genome analyses reveal the dynamic evolutionary history of octopod chromosomes and neural-related gene families. The single-nucleus transcriptomes of the supra-esophageal brain indicate their cellular heterogeneities and functional interactions with other tissues (i.e., gill), which provides a foundation for further octopod cerebral studies.

Abstract 5141: Using single cell gene expression to develop treatment response predictions in multiple myeloma

Abstract The promise of precision medicine in oncology is to identify the ideal treatment for each patient, eliminating failed treatment cycles and reducing treatment burdens on patients and payors. While gene sequencing can predict effective treatment options in some cases, studies have estimated that only 10-15% of cancer patients are treated with genotype matched drugs. Additional treatment selection tools for predicting patient response thus remains a critical clinical need. While gene sequencing has clear utility in predicting therapeutic response, gene expression is less commonly used in clinical tests to guide treatment selection. Gene expression is theoretically attractive to predicting therapeutic response because, unlike mutational analysis, it provides a direct readout of the dysregulated biochemical activities in the cells which may be targeted by particular therapies. One of the major problems with conventional approaches to using gene expression to predict therapeutic response is tumor heterogeneity; that is, a biopsied tumor contains multiple cells types and even different tumor clones that can confound a conventional bulk gene expression analysis. More recently, single-cell RNA expression analysis technologies have grown in popularity. These technologies enable the fine characterization of tumors and their cellular makeup and hold the promise of increasing the power of gene expression measurements for identifying therapy response prediction tools. Here we show the application of single cell gene expression analysis with Proteona’s MapResponse™ machine-learning algorithms to developing a predictive classifier for patient response to Daratumumab treatment in multiple myeloma. Starting with publicly available data (Cohen et al. Nat. Medicine 2021), we developed a classifier that accurately predicted the response of 94% of subjects in the published study. This classifier was able to accurately predict response in an independent cohort of Daratumumab-treated patients. In contrast, the classifier performed poorly in a cohort of patients who did not receive Daratumumab. Together these data suggest the classifier has specificity to predicting response to Daratumumab and is not a general prognostic signature. These early findings point to the potential value of combining single-cell RNA sequencing with machine-learning methods such as MapResponse™ to bring precision medicine to more patients. Citation Format: Jonathan Scolnick, Stacy Xu, Sanjay de Mel, Cinnie Yentia Soekojo, Wee Joo Chng. Using single cell gene expression to develop treatment response predictions in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5141.

Expression of a BTB-ZF transcription factor controls the function of an innate-like Tregs essential for intestinal homeostasis

Abstract The BTB-ZF genes are a family of 49 transcription factors that control the lineage specification and development of key effector functions of different subsets of lymphocytes. Using a single-cell BTB-ZF transcription factor expression analysis, we identified a unique subset of T regulatory cells (Tregs). These Tregs are genetically and phenotypically distinct from the larger conventional Treg population. For instance, analogous to natural killer (NKT) cells they have an activated phenotype (CD62Llo, CD44hi) and constitutively express IL-10. Interestingly, these characteristics are acquired early during T cell development. The defining transcription factor is induced during thymic development, binds the IL-10 promoter, and enables T cells to rapidly produce the cytokine. The subset of Tregs is enriched in the intestine and expands during acute colitis. The deletion of the defining transcription factor in T cells results in intestinal epithelial layer damage causing loss of the barrier function making mice highly vulnerable to severe disease and death from induced colitis. Moreover, in the absence of the transcription factor in T cells, normal intestinal macrophage responses are disrupted most likely due to decreased production of IL-10 by the Tregs. Therefore, by profiling BTB-ZF transcription factor expression, we have identified a distinct subset of T cells with potent immunosuppressive abilities that are essential for the health of the intestine. Numerous aspects of these Tregs are parallel to NKT cells, indicating an existence of “naïve” Tregs that have innate-like effector functions acquired independently from the differentiation in the periphery. Supported by NIH/NIAID R01 AI122757-01 Careers in Immunology Fellowships – 2021

Pembrolizumab and decitabine for refractory or relapsed acute myeloid leukemia

BackgroundThe powerful ‘graft versus leukemia’ effect thought partly responsible for the therapeutic effect of allogeneic hematopoietic cell transplantation in acute myeloid leukemia (AML) provides rationale for investigation of immune-based therapies...

Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis

Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis

Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis

Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis

Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr

Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.

Targeting 3' and 5' untranslated regions with antisense oligonucleotides to stabilize frataxin mRNA and increase protein expression.

Friedreich’s ataxia (FRDA) is a severe multisystem disease caused by transcriptional repression induced by expanded GAA repeats located in intron 1 of the Frataxin (FXN) gene encoding frataxin. FRDA results from decreased levels of frataxin; thus, stabilization of the FXN mRNA already present in patient cells represents an attractive and unexplored therapeutic avenue. In this work, we pursued a novel approach based on oligonucleotide-mediated targeting of FXN mRNA ends to extend its half-life and availability as a template for translation. We demonstrated that oligonucleotides designed to bind to FXN 5′ or 3′ noncoding regions can increase FXN mRNA and protein levels. Simultaneous delivery of oligonucleotides targeting both ends increases efficacy of the treatment. The approach was confirmed in several FRDA fibroblast and induced pluripotent stem cell-derived neuronal progenitor lines. RNA sequencing and single-cell expression analyses confirmed oligonucleotide-mediated FXN mRNA upregulation. Mechanistically, a significant elongation of the FXN mRNA half-life without any changes in chromatin status at the FXN gene was observed upon treatment with end-targeting oligonucleotides, indicating that transcript stabilization is responsible for frataxin upregulation. These results identify a novel approach toward upregulation of steady-state mRNA levels via oligonucleotide-mediated end targeting that may be of significance to any condition resulting from transcription downregulation.

Schwann Cells Are Required for Efficient Corneal Wound Healing

BACKGROUND: Corneal nerves play a crucial role in maintaining corneal health, which includes regulation of activity of limbal stem cell (LSC). Their loss leads to neurotrophic keratopathy (NK), with corneal ulceration, scarring, and ultimately, blindness.1 Having identified nerve-ensheathing Schwann cells (SC) in the corneal limbus, we hypothesize that SCs, via paracrine interaction with LSC, play a key role in corneal epithelial maintenance and healing. In this study we wanted to (1) Define the role of SCs in corneal healing; (2) Determine the paracrine interaction between the limbal SCs and LSC. METHODS: (1) Local corneal ablation of SCs was induced in a genetically modified mouse where the topical application of tamoxifen induced SCs apoptosis.2 The corneal epithelium was then removed with an Amoils brush under anesthesia and fluorescein was used to assess healing over 4 days. (2) We performed single-cell RNA expression analysis of 10,000 cells derived from dissociated rat limbus with droplet-based high throughput 10× Genomics to identify ~3000 genes.3 We used the data to predict possible ligand-receptor interactions between the limbal SCs and LSC. RESULTS: (1) Ablation of SCs impaired corneal wound healing in mouse cornea, suggesting the involvement of SC in innervation-dependent corneal epithelial recovery. (2) Genomic analysis suggested the presence of paracrine crosstalk between SCs and LSCs, and relevant downstream intracellular signaling events in LSCs. The latter included activation of Notch signaling and VEGF-mediated cell migration and inhibition of apoptosis.4 Further expression analysis comparing the limbal region of healthy and wounded corneas indicated significant changes in the expression of jag1, Pdgfa, Tgfb1, and Ptn genes by SCs. All of these genes could potentially play a role in corneal recovery. CONCLUSIONS: Our findings (i) describe the presence of a high volume of SCs at the limbus, located in close spatial vicinity to LSCs, (ii) demonstrate the importance of the limbal SCs for corneal wound healing, and (iii) suggest the presence of paracrine SC-LSC interaction that may be responsible for the limbal nerve-mediated activation of LSCs during homeostasis or the epithelial wound healing after injury. These findings suggest new therapeutic targets for treating NK. REFERENCES: 1. Dua HS, Said DG, Messmer EM, et al. Neurotrophic keratopathy. Prog Retin Eye Res. 2018 Sep;66:107-131. 2. Johnston APW, Naska S, Jones K, et al. Sox2-mediated regulation of adult neural crest precursors and skin repair. Stem Cell Rep. 2013;1(1):38–45. doi:10.1016/j.stemcr.2013.04.004 3. Yuzwa SA, Borrett MJ, Innes BT, et al. Developmental emergence of adult neural stem cells as revealed by single-cell transcriptional profiling. Cell Rep. 2017;21(13):3970–3986. doi:10.1016/j.celrep.2017.12.017 4. Lindsell CE, Shawber CJ, Boulter J, et al. Jagged: A mammalian ligand that activates notch1. Cell. 1995;80(6):909–917. doi:10.1016/0092-8674(95)90294-5