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Cerebral cortical neuron diversity and development at single-cell resolution.
Over a century of efforts to categorize the astonishing diversity of cortical neurons has relied on criteria of morphology, electrophysiology, ontology, and the expression of a few transcripts and proteins. The rapid development of single-cell RNA sequencing (scRNA-seq) adds genome-wide gene expression patterns to this list of criteria, and promises to reveal new insights into the transitions that establish neuronal identity during development, differentiation, activity, and disease. Comparing single neuron data to reference atlases constructed from hundreds of thousands of single-cell transcriptomes will be critical to understanding these transitions and the molecular mechanisms that drive them. We review early efforts, and discuss future challenges and opportunities, in applying scRNA-seq to the elucidation of neuronal subtypes and their development.
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Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility.
Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.
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The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas.
A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans.
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Integrative genomic profiling identifies MLPH as a candidate gene in prostate cancer.
BACKGROUND: Prostate cancer (PCa) is a highly heterogeneous malignancy with complex genetic underpinnings. This study integrates multi-omics data to prioritize candidate susceptibility genes and evaluate their functional and clinical significance in PCa pathogenesis. MATERIALS AND METHODS: We integrated PCa GWAS summary statistics with GTEx v8 expression quantitative trait locus reference panels to perform cross-tissue and single-tissue transcriptome-wide association studies. Candidate signals were refined using conditional analysis, MAGMA and fastBAT gene-level tests, Summary data-based Mendelian randomization, and Bayesian colocalization. Tumor-context cis-eQTL evidence from TCGA-PRAD was incorporated to prioritize regulatory signals retained in prostate cancer tissues. Prioritized candidates were further assessed using transcriptomic datasets, Human Protein Atlas immunohistochemistry, single-cell RNA-seq analysis, histological grading, preoperative PSA, and established genomic risk signatures. Gene network and pathway enrichment analyses were performed to explore potential biological context. RESULTS: The integrative genetic analyses identified 23 consensus candidate genes supported by multiple association frameworks. SMR and Bayesian colocalization further narrowed the candidate list, and tumor-context cis-eQTL analysis in TCGA-PRAD retained MLPH as the final prioritized candidate. The lead variant rs7582964 was significantly associated with MLPH expression in PRAD tumor tissues. MLPH was upregulated in PCa tissues compared with normal prostate tissues in TCGA-PRAD and showed concordant expression patterns in an independent GEO cohort and Human Protein Atlas immunohistochemistry data. Single-cell transcriptomic analysis localized MLPH expression mainly to epithelial cells, with the strongest signal observed in tumor epithelial cells. Clinically, MLPH expression was associated with histological differentiation, with reduced expression in the most poorly differentiated tumors. Lower MLPH expression also correlated with higher preoperative PSA and higher Decipher-like genomic risk scores. Functional analyses linked MLPH to vesicle-mediated transport, exocytosis, and hormone-related signaling pathways. DISCUSSION AND CONCLUSION: This integrative genomic analysis prioritizes MLPH as a candidate susceptibility gene for PCa and links its regulatory signal to tumor-context expression, protein-level evidence, cellular localization, and clinically relevant molecular features. These findings support a potential role for MLPH in PCa biology, particularly in relation to vesicle trafficking and tumor differentiation.
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Analysis of meristems and plant regeneration at single-cell resolution.
Rapid development of high-throughput single-cell RNA sequencing (scRNA-seq) technologies offers exciting opportunities to reveal new and rare cell types, previously hidden cell states, and continuous developmental trajectories. In this review, we first illustrate the ways in which scRNA-seq enables researchers to distinguish between distinct plant cell populations, delineate cell cycle continuums, and infer continuous differentiation trajectories of diverse cell types in shoots, roots, and floral and vascular meristems with unprecedented resolution. We then highlight the emerging power of scRNA-seq to dissect cell heterogeneity in regenerating tissues and uncover the cellular basis of cell reprogramming and stem cell commitment during plant regeneration. We conclude by discussing related outstanding questions in the field.
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Overexpression of transposable elements is associated with immune evasion and poor outcome in colorectal cancer.
AIM: High immune cell infiltration of the tumour microenvironment is generally associated with a good prognosis in solid cancers. However, a subset of patients with colorectal cancer (CRC) tumours with high immune cell infiltration have a poor outcome. These tumours have a high level of T cell infiltration and are also characterised by increased expression of programmed death-ligand 1 (PD-L1). As these tumours comprise both microsatellite instability and microsatellite stable subtypes, the mechanism underlying this phenotype is unknown. METHODS: Using RNA-seq data from The Cancer Genome Atlas, we quantified transposable element (TE) expression and developed a TE expression score that is predictive of prognosis and immune infiltration independent of microsatellite instability status and tumour staging in CRC. RESULTS: Tumours with the highest TE expression score showed increased immune cell infiltration with upregulation of interferon (IFN) signalling pathways and downstream activation of IFN-simulated genes. As expected, cell lines treated with DNA methyltransferase inhibitor mimicked patient tumours with increased TE expression and IFN signalling. However, surprisingly, unlike high TE expressing CRC, there is little evidence for the activation of JAK-STAT signalling and PD-L1 expression in DNA methyltransferase inhibitor-treated cells. Single-cell RNA-seq analysis of CRC samples showed that PD-L1 expression is mainly confined to tumour-associated macrophages and T cells, suggesting that TE mediated IFN signalling is triggering expression of PD-L1 in immune cells rather than in tumour cells. CONCLUSIONS: Our study uncovers a novel mechanism of TE driven immune evasion and highlights TE expression as an important factor for patient prognosis in CRC.
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Upregulated PPP1R14B is connected to cancer progression and immune infiltration in kidney renal clear cell carcinoma.
BACKGROUND: Protein phosphatase 1 regulatory subunit 14B (PPP1R14B) is an oncogenic gene found in a variety of tumors, but its role in the prognosis and development of kidney renal clear cell carcinoma (KIRC) remains unknown. Our study aimed to determine whether PPP1R14B could be a prognostic biomarker for KIRC and its role in the development of KIRC. METHODS: In this work, we used The Cancer Genome Atlas (TCGA) database to explore the expression of PPP1R14B in tumor tissues, its relationship with the prognosis of tumor patients, and its role in tumor occurrence and development. We validated our findings using the International Cancer Genome Consortium (ICGC) cohort, our clinical samples, and in vitro experiments. RESULTS: PPP1R14B was upregulated in KIRC compared to adjacent normal tissue. Moreover, multivariate analysis revealed that upregulated PPP1R14B expression was an independent risk factor for KIRC progression. High-PPP1R14B groups had shorter overall survival (OS) and disease-free survival (DFS) in TCGA and ICGC cohorts. We used Cell Counting Kit-8 (CCK8) and scratch wound healing assay to explore the proliferation and migration of KIRC cells following PPP1R14B knockdown. Our results indicated that PPP1R14B knockdown significantly reduced the proliferation and migration of KIRC cells in vitro. We also explored the possible cellular mechanisms of PPP1R14B through the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) analysis, and TISIDB analysis. The function enrich analysis revealed that PPP1R14B-related genes were mainly enriched in purine metabolism and the macromolecule catabolic process. PPP1R14B expression was associated with tumor-infiltrating immune cells (TIICs) in the TCGA cohort, and the results of single-cell RNA-seq (scRNA) further demonstrated that PPP1R14B expression was associated with the enhanced infiltration of CD8 + T lymphocytes. CONCLUSION: PPP1R14B may serve as a prognostic biomarker in KIRC, affect purine metabolism, activate immune infiltration, and promote KIRC cell migration.
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Singletrome enhances detection of long noncoding RNAs in single cell transcriptomes.
Single cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression in individual cell types, but scRNA-seq studies have focused primarily on expression of protein-coding genes. Long noncoding RNAs (lncRNAs) are more diverse than protein-coding genes, yet remain underexplored in part because they are underrepresented in reference annotations applied to scRNA-seq. Merging annotations containing protein-coding and lncRNA genes is not sufficient, because the addition of lncRNA genes that overlap in sense and antisense with protein-coding genes will affect how reads are counted for both protein-coding and lncRNA genes. Here, we introduce Singletrome, a Singularity image that integrates protein-coding and lncRNA gene transfer format (GTF) annotations to generate enhanced annotations that take into account the sense and antisense overlap of annotated genes, maps scRNA-seq data, and produces files for downstream analysis and visualization. With Singletrome, we detected thousands of lncRNAs not included in GENCODE, clustered cell types based solely on lncRNA expression, and demonstrated that machine learning can predict cell type and disease through lncRNAs alone. This comprehensive annotation will allow mapping of lncRNA expression across cell types of the human body, facilitating the development of an atlas of human lncRNAs in health and disease with the ability to integrate new lncRNA annotations as they become available.
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ACTINN: automated identification of cell types in single cell RNA sequencing.
MOTIVATION: Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. RESULTS: We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. AVAILABILITY AND IMPLEMENTATION: The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC.
Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.