Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

The Biology and Genomics of Human Hair Follicles: A Focus on Androgenetic Alopecia.

Androgenetic alopecia is a highly prevalent condition mainly affecting men. This complex trait is related to aging and genetics; however, multiple other factors, for example, lifestyle, are also involved. Despite its prevalence, the underlying biology of androgenetic alopecia remains elusive, and thus advances in its treatment have been hindered. Herein, we review the functional anatomy of hair follicles and the cell signaling events that play a role in follicle cycling. We also discuss the pathology of androgenetic alopecia and the known molecular mechanisms underlying this condition. Additionally, we describe studies comparing the transcriptional differences in hair follicles between balding and non-balding scalp regions. Given the genetic contribution, we also discuss the most significant risk variants found to be associated with androgenetic alopecia. A more comprehensive understanding of this pathology may be generated through using multi-omics approaches.

Exploring the Potential Role of Circulating microRNAs as Biomarkers for Predicting Clinical Response to Neoadjuvant Therapy in Breast Cancer.

Breast cancer (BC) is a leading cause of cancer-related deaths among women worldwide. Neoadjuvant therapy (NAT) is increasingly being used to reduce tumor burden prior to surgical resection. However, current techniques for assessing tumor response have significant limitations. Additionally, drug resistance is commonly observed, raising a need to identify biomarkers that can predict treatment sensitivity and survival outcomes. Circulating microRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and have been shown to play a significant role in cancer progression as tumor inducers or suppressors. The expression of circulating miRNAs has been found to be significantly altered in breast cancer patients. Moreover, recent studies have suggested that circulating miRNAs can serve as non-invasive biomarkers for predicting response to NAT. Therefore, this review provides a brief overview of recent studies that have demonstrated the potential of circulating miRNAs as biomarkers for predicting the clinical response to NAT in BC patients. The findings of this review will strengthen future research on developing miRNA-based biomarkers and their translation into medical practice, which could significantly improve the clinical management of BC patients undergoing NAT.

Glial progenitor heterogeneity and key regulators revealed by single-cell RNA sequencing provide insight to regeneration in spinal cord injury.

Recent studies have revealed the heterogeneous nature of astrocytes; however, how diverse constituents of astrocyte-lineage cells are regulated in adult spinal cord after injury and contribute to regeneration remains elusive. We perform single-cell RNA sequencing of GFAP-expressing cells from sub-chronic spinal cord injury models and identify and compare with the subpopulations in acute-stage data. We find subpopulations with distinct functional enrichment and their identities defined by subpopulation-specific transcription factors and regulons. Immunohistochemistry, RNAscope experiments, and quantification by stereology verify the molecular signature, location, and morphology of potential resident neural progenitors or neural stem cells in the adult spinal cord before and after injury and uncover the populations of the intermediate cells enriched in neuronal genes that could potentially transition into other subpopulations. This study has expanded the knowledge of the heterogeneity and cell state transition of glial progenitors in adult spinal cord before and after injury.

Major Differences in Transcriptional Alterations in Dorsal Root Ganglia Between Spinal Cord Injury and Peripheral Neuropathic Pain Models.

Chronic, often intractable, pain is caused by neuropathic conditions such as traumatic peripheral nerve injury (PNI) and spinal cord injury (SCI). These conditions are associated with alterations in gene and protein expression correlated with functional changes in somatosensory neurons having cell bodies in dorsal root ganglia (DRGs). Most studies of DRG transcriptional alterations have utilized PNI models where axotomy-induced changes important for neural regeneration may overshadow changes that drive neuropathic pain. Both PNI and SCI produce DRG neuron hyperexcitability linked to pain, but contusive SCI produces little peripheral axotomy or peripheral nerve inflammation. Thus, comparison of transcriptional signatures of DRGs across PNI and SCI models may highlight pain-associated transcriptional alterations in sensory ganglia that do not depend on peripheral axotomy or associated effects such as peripheral Wallerian degeneration. Data from our rat thoracic SCI experiments were combined with meta-analysis of published whole-DRG RNA-seq datasets from prominent rat PNI models. Striking differences were found between transcriptional responses to PNI and SCI, especially in regeneration-associated genes (RAGs) and long noncoding RNAs (lncRNAs). Many transcriptomic changes after SCI also were found after corresponding sham surgery, indicating they were caused by injury to surrounding tissue, including bone and muscle, rather than to the spinal cord itself. Another unexpected finding was of few transcriptomic similarities between rat neuropathic pain models and the only reported transcriptional analysis of human DRGs linked to neuropathic pain. These findings show that DRGs exhibit complex transcriptional responses to central and peripheral neural injury and associated tissue damage. Although only a few genes in DRG cells exhibited similar changes in expression across all the painful conditions examined here, these genes may represent a core set whose transcription in various DRG cell types is sensitive to significant bodily injury, and which may play a fundamental role in promoting neuropathic pain.

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases.

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

Epigenetics of neural differentiation: Spotlight on enhancers.

Neural induction, both in vivo and in vitro, includes cellular and molecular changes that result in phenotypic specialization related to specific transcriptional patterns. These changes are achieved through the implementation of complex gene regulatory networks. Furthermore, these regulatory networks are influenced by epigenetic mechanisms that drive cell heterogeneity and cell-type specificity, in a controlled and complex manner. Epigenetic marks, such as DNA methylation and histone residue modifications, are highly dynamic and stage-specific during neurogenesis. Genome-wide assessment of these modifications has allowed the identification of distinct non-coding regulatory regions involved in neural cell differentiation, maturation, and plasticity. Enhancers are short DNA regulatory regions that bind transcription factors (TFs) and interact with gene promoters to increase transcriptional activity. They are of special interest in neuroscience because they are enriched in neurons and underlie the cell-type-specificity and dynamic gene expression profiles. Classification of the full epigenomic landscape of neural subtypes is important to better understand gene regulation in brain health and during diseases. Advances in novel next-generation high-throughput sequencing technologies, genome editing, Genome-wide association studies (GWAS), stem cell differentiation, and brain organoids are allowing researchers to study brain development and neurodegenerative diseases with an unprecedented resolution. Herein, we describe important epigenetic mechanisms related to neurogenesis in mammals. We focus on the potential roles of neural enhancers in neurogenesis, cell-fate commitment, and neuronal plasticity. We review recent findings on epigenetic regulatory mechanisms involved in neurogenesis and discuss how sequence variations within enhancers may be associated with genetic risk for neurological and psychiatric disorders.

Dynamic landscape of chromatin accessibility and transcriptomic changes during differentiation of human embryonic stem cells into dopaminergic neurons.

Chromatin architecture influences transcription by modulating the physical access of regulatory factors to DNA, playing fundamental roles in cell identity. Studies on dopaminergic differentiation have identified coding genes, but the relationship with non-coding genes or chromatin accessibility remains elusive. Using RNA-Seq and ATAC-Seq we profiled differentially expressed transcripts and open chromatin regions during early dopaminergic neuron differentiation. Hierarchical clustering of differentially expressed genes, resulted in 6 groups with unique characteristics. Surprisingly, the abundance of long non-coding RNAs (lncRNAs) was high in the most downregulated transcripts, and depicted positive correlations with target mRNAs. We observed that open chromatin regions decrease upon differentiation. Enrichment analyses of accessibility depict an association between open chromatin regions and specific functional pathways and gene-sets. A bioinformatic search for motifs allowed us to identify transcription factors and structural nuclear proteins that potentially regulate dopaminergic differentiation. Interestingly, we also found changes in protein and mRNA abundance of the CCCTC-binding factor, CTCF, which participates in genome organization and gene expression. Furthermore, assays demonstrated co-localization of CTCF with Polycomb-repressed chromatin marked by H3K27me3 in pluripotent cells, progressively decreasing in neural precursor cells and differentiated neurons. Our work provides a unique resource of transcription factors and regulatory elements, potentially involved in the acquisition of human dopaminergic neuron cell identity.

Brain Region-Specific Gene Signatures Revealed by Distinct Astrocyte Subpopulations Unveil Links to Glioma and Neurodegenerative Diseases.

Currently, there are no effective treatments for glioma or for neurodegenerative diseases because of, in part, our limited understanding of the pathophysiology and cellular heterogeneity of these diseases. Mounting evidence suggests that astrocytes play an active role in the pathogenesis of these diseases by contributing to a diverse range of pathophysiological states. In a previous study, five molecularly distinct astrocyte subpopulations from three different brain regions were identified. To further delineate the underlying diversity of these populations, we obtained mouse brain region-specific gene signatures for both protein-coding and long non-coding RNA and found that these astrocyte subpopulations are endowed with unique molecular signatures across diverse brain regions. Additional gene set and single-sample enrichment analyses revealed that gene signatures of different subpopulations are differentially correlated with glioma tumors that harbor distinct genomic alterations. To the best of our knowledge, this is the first study that links transcriptional profiles of astrocyte subpopulations with glioma genomic mutations. Furthermore, our results demonstrated that subpopulations of astrocytes in select brain regions are associated with specific neurodegenerative diseases. Overall, the present study provides a new perspective for understanding the pathophysiology of glioma and neurodegenerative diseases and highlights the potential contributions of diverse astrocyte populations to normal, malignant, and degenerative brain functions.

An integrated global regulatory network of hematopoietic precursor cell self-renewal and differentiation.

Systematic study of the regulatory mechanisms of Hematopoietic Stem Cell and Progenitor Cell (HSPC) self-renewal is fundamentally important for understanding hematopoiesis and for manipulating HSPCs for therapeutic purposes. Previously, we have characterized gene expression and identified important transcription factors (TFs) regulating the switch between self-renewal and differentiation in a multipotent Hematopoietic Progenitor Cell (HPC) line, EML (Erythroid, Myeloid, and Lymphoid) cells. Herein, we report binding maps for additional TFs (SOX4 and STAT3) by using chromatin immunoprecipitation (ChIP)-Sequencing, to address the underlying mechanisms regulating self-renewal properties of lineage-CD34+ subpopulation (Lin-CD34+ EML cells). Furthermore, we applied the Assay for Transposase Accessible Chromatin (ATAC)-Sequencing to globally identify the open chromatin regions associated with TF binding in the self-renewing Lin-CD34+ EML cells. Mass spectrometry (MS) was also used to quantify protein relative expression levels. Finally, by integrating the protein-protein interaction database, we built an expanded transcriptional regulatory and interaction network. We found that MAPK (Mitogen-activated protein kinase) pathway and TGF-ß/SMAD signaling pathway components were highly enriched among the binding targets of these TFs in Lin-CD34+ EML cells. The present study integrates regulatory information at multiple levels to paint a more comprehensive picture of the HSPC self-renewal mechanisms.

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis.

In mammalian cells, gene transcription is regulated in a cell type specific manner by the interactions of transcriptional factors with genomic DNA. Lineage-specific transcription factors are considered to play essential roles in cell specification and differentiation during development. ChIP coupled with high-throughput DNA sequencing (ChIP-seq) is widely used to analyze genome-wide binding sites of transcription factors (or its associated complex) to genomic DNA. However, a large number of cells are required for one standard ChIP reaction, which makes it difficult to study the limited number of isolated primary cells or rare cell populations. In order to understand the regulatory mechanism of oligodendrocyte lineage-specific transcription factor Olig2 in acutely purified mouse OPCs, a detailed method using ChIP-seq to identify the genome-wide binding sites of Olig2 (or Olig2 complex) is shown. First, the protocol explains how to purify the platelet-derived growth factor receptor alpha (PDGFRα) positive OPCs from mouse brains. Next, Olig2 antibody mediated ChIP and library construction are performed. The last part describes the bioinformatic software and procedures used for Olig2 ChIP-seq analysis. In summary, this paper reports a method to analyze the genome-wide bindings of transcriptional factor Olig2 in acutely purified brain OPCs.

Single-cell RNA-sequencing of the brain.

Single-cell RNA-sequencing (scRNA-seq) is revolutionizing our understanding of the genomic, transcriptomic and epigenomic landscapes of cells within organs. The mammalian brain is composed of a complex network of millions to billions of diverse cells with either highly specialized functions or support functions. With scRNA-seq it is possible to comprehensively dissect the cellular heterogeneity of brain cells, and elucidate their specific functions and state. In this review, we describe the current experimental methods used for scRNA-seq. We also review bioinformatic tools and algorithms for data analyses and discuss critical challenges. Additionally, we summarized recent mouse brain scRNA-seq studies and systematically compared their main experimental approaches, computational tools implemented, and important findings. scRNA-seq has allowed researchers to identify diverse cell subpopulations within many brain regions, pinpointing gene signatures and novel cell markers, as well as addressing functional differences. Due to the complexity of the brain, a great deal of work remains to be accomplished. Defining specific brain cell types and functions is critical for understanding brain function as a whole in development, health, and diseases.

Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination.

Long non-coding RNAs (lncRNAs) (> 200 bp) play crucial roles in transcriptional regulation during numerous biological processes. However, it is challenging to comprehensively identify lncRNAs, because they are often expressed at low levels and with more cell-type specificity than are protein-coding genes. In the present study, we performed ab initio transcriptome reconstruction using eight purified cell populations from mouse cortex and detected more than 5000 lncRNAs. Predicting the functions of lncRNAs using cell-type specific data revealed their potential functional roles in Central Nervous System (CNS) development. We performed motif searches in ENCODE DNase I digital footprint data and Mouse ENCODE promoters to infer transcription factor (TF) occupancy. By integrating TF binding and cell-type specific transcriptomic data, we constructed a novel framework that is useful for systematically identifying lncRNAs that are potentially essential for brain cell fate determination. Based on this integrative analysis, we identified lncRNAs that are regulated during Oligodendrocyte Precursor Cell (OPC) differentiation from Neural Stem Cells (NSCs) and that are likely to be involved in oligodendrogenesis. The top candidate, lnc-OPC, shows highly specific expression in OPCs and remarkable sequence conservation among placental mammals. Interestingly, lnc-OPC is significantly up-regulated in glial progenitors from experimental autoimmune encephalomyelitis (EAE) mouse models compared to wild-type mice. OLIG2-binding sites in the upstream regulatory region of lnc-OPC were identified by ChIP (chromatin immunoprecipitation)-Sequencing and validated by luciferase assays. Loss-of-function experiments confirmed that lnc-OPC plays a functional role in OPC genesis. Overall, our results substantiated the role of lncRNA in OPC fate determination and provided an unprecedented data source for future functional investigations in CNS cell types. We present our datasets and analysis results via the interactive genome browser at our laboratory website that is freely accessible to the research community. This is the first lncRNA expression database of collective populations of glia, vascular cells, and neurons. We anticipate that these studies will advance the knowledge of this major class of non-coding genes and their potential roles in neurological development and diseases.

A role for 1α,25-dihydroxyvitamin d3 in the expression of circadian genes.

The active form of vitamin D, 1α,25-(OH)2D3, has been associated with metabolism control, cell growth, differentiation, antiproliferation, apoptosis, and adaptive/innate immune responses, besides its functions in the integrity of bone and calcium homeostasis. The circadian rhythm regulates a variety of biological processes, many of them related to the functions associated with 1α,25-(OH)2D3. In the present study, we determine whether 1α,25-(OH)2D3 alters the expression of circadian genes in adipose-derived stem cells (ADSCs). The effect of 1α,25-(OH)2D3 on the expression of circadian genes BMAL1 and PER2 was measured by qPCR, over a 60-h period every 4 h, in serum shocked ADSCs, serum shocked ADSCs supplemented with 1α,25-(OH)2D3, and ADSCs under the presence of only 1α,25-(OH)2D3. The results showed that 1α,25-(OH)2D3 was able to synchronize circadian clock gene expression in ADSCs. The expression of circadian genes BMAL1 and PER2 in ADSCs that contained only 1α,25-(OH)2D3 has a profile similar to that found in the ADSCs synchronized by a serum shock. The results suggest an important role of 1α,25-(OH)2D3 in the regulation of the molecular clock.

Age-related yield of adipose-derived stem cells bearing the low-affinity nerve growth factor receptor.

Adipose-derived stem cells (ADSCs) are a heterogeneous cell population that may be enriched by positive selection with antibodies against the low-affinity nerve growth factor receptor (LNGFR or CD271), yielding a selective cell universe with higher proliferation and differentiation potential. This paper addresses the need for determining the quantity of ADSCs positive for the CD271 receptor and its correlation with donor's age. Mononuclear cells were harvested from the lower backs of 35 female donors and purified using magnetic beads. Multipotency capacity was tested by the expression of stemness genes and through differentiation into preosteoblasts and adipocytes. A significant statistical difference was found in CD271(+) concentrations between defined age intervals. The highest yield was found within women on the 30-40-year-old age range. CD271(+) ADSCs from all age groups showed differentiation capabilities as well as expression of typical multipotent stem cell genes. Our data suggest that the amount of CD271(+) cells correlates inversely with age. However, the ability to obtain these cells was maintained through all age ranges with a yield higher than what has been reported from bone marrow. Our findings propose CD271(+) ADSCs as the primary choice for tissue regeneration and autologous stem cell therapies in older subjects.

COMPADRE: an R and web resource for pathway activity analysis by component decompositions.

UNLABELLED: The analysis of biological networks has become essential to study functional genomic data. Compadre is a tool to estimate pathway/gene sets activity indexes using sub-matrix decompositions for biological networks analyses. The Compadre pipeline also includes one of the direct uses of activity indexes to detect altered gene sets. For this, the gene expression sub-matrix of a gene set is decomposed into components, which are used to test differences between groups of samples. This procedure is performed with and without differentially expressed genes to decrease false calls. During this process, Compadre also performs an over-representation test. Compadre already implements four decomposition methods [principal component analysis (PCA), Isomaps, independent component analysis (ICA) and non-negative matrix factorization (NMF)], six statistical tests (t- and f-test, SAM, Kruskal-Wallis, Welch and Brown-Forsythe), several gene sets (KEGG, BioCarta, Reactome, GO and MsigDB) and can be easily expanded. Our simulation results shown in Supplementary Information suggest that Compadre detects more pathways than over-representation tools like David, Babelomics and Webgestalt and less false positives than PLAGE. The output is composed of results from decomposition and over-representation analyses providing a more complete biological picture. Examples provided in Supplementary Information show the utility, versatility and simplicity of Compadre for analyses of biological networks. AVAILABILITY AND IMPLEMENTATION: Compadre is freely available at http://bioinformatica.mty.itesm.mx:8080/compadre. The R package is also available at https://sourceforge.net/p/compadre.