Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research

Single-cell RNA sequencing technology (scRNA-seq) has been steadily developing since its inception in 2009. Unlike bulk RNA-seq, scRNA-seq identifies the heterogeneity of tissue cells and reveals gene expression changes in individual cells at the microscopic level. Here, we review the development of scRNA-seq, which has gone through iterations of reverse transcription, in vitro transcription, smart-seq, drop-seq, 10 × Genomics, and spatial single-cell transcriptome technologies. The technology of 10 × Genomics has been widely applied in medicine and biology, producing rich research results. Furthermore, this review presents a summary of the analytical process for single-cell transcriptome data and its integration with other omics analyses, including genomes, epigenomes, proteomes, and metabolomics. The single-cell transcriptome has a wide range of applications in biology and medicine. This review analyzes the applications of scRNA-seq in cancer, stem cell research, developmental biology, microbiology, and other fields. In essence, scRNA-seq provides a means of elucidating gene expression patterns in single cells, thereby offering a valuable tool for scientific research. Nevertheless, the current single-cell transcriptome technology is still imperfect, and this review identifies its shortcomings and anticipates future developments. The objective of this review is to facilitate a deeper comprehension of scRNA-seq technology and its applications in biological and medical research, as well as to identify avenues for its future development in alignment with practical needs.

Bi-allelic variants in chromatoid body protein TDRD6 cause spermiogenesis defects and severe oligoasthenoteratozoospermia in humans

BACKGROUND

The association between the TDRD6 variants and human infertility remains unclear, as only one homozygous missense variant of TDRD6 was found to be associated with oligoasthenoteratozoospermia (OAT).

METHODS

Whole-exome sequencing and Sanger sequencing were employed to identify potential pathogenic variants of TDRD6 in infertile men. Histology, immunofluorescence, immunoblotting and ultrastructural analyses were conducted to clarify the structural and functional abnormalities of sperm in mutated patients. Tdrd6-knockout mice were generated using the CRISPR-Cas9 system. Total RNA-seq and single-cell RNA-seq (scRNA-seq) analyses were used to elucidate the underlying molecular mechanisms, followed by validation through quantitative RT-PCR and immunostaining. Intracytoplasmic sperm injection (ICSI) was also used to assess the efficacy of clinical treatment.

RESULTS

Bi-allelic TDRD6 variants were identified in five unrelated Chinese individuals with OAT, including homozygous loss-of-function variants in two consanguineous families. Notably, besides reduced concentrations and impaired motility, a significant occurrence of acrosomal hypoplasia was detected in multiple spermatozoa among five patients. Using the Tdrd6-deficient mice, we further elucidate the pivotal role of TDRD6 in spermiogenesis and acrosome identified. In addition, the mislocalisation of crucial chromatoid body components DDX4 (MVH) and UPF1 was also observed in round spermatids from patients harbouring TDRD6 variants. ScRNA-seq analysis of germ cells from a patient with TDRD6 variants revealed that TDRD6 regulates mRNA metabolism processes involved in spermatid differentiation and cytoplasmic translation.

CONCLUSION

Our findings strongly suggest that TDRD6 plays a conserved role in spermiogenesis and confirms the causal relationship between TDRD6 variants and human OAT. Additionally, this study highlights the unfavourable ICSI outcomes in individuals with bi-allelic TDRD6 variants, providing insights for potential clinical treatment strategies.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model

OBJECTIVE

The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients.

METHODS

Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights.

RESULTS

High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1.

CONCLUSION

The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

Comprehensive analysis of juvenile idiopathic arthritis patients' immune characteristics based on bulk and single-cell sequencing data

Background

The pathogenesis of juvenile idiopathic arthritis (JIA) is strongly influenced by an impaired immune system. However, the molecular mechanisms underlying its development and progression have not been elucidated. In this study, the computational methods TRUST4 were used to construct a T-cell receptor (TCR) and B-cell receptor (BCR) repertoire from the peripheral blood of JIA patients via bulk RNA-seq data, after which the clonality and diversity of the immune repertoire were analyzed.

Results

Our findings revealed significant differences in the frequency of clonotypes between the JIA and healthy control groups in terms of the TCR and BCR repertoires. This work identified specific V genes and J genes in TCRs and BCRs that could be used to expand our understanding of JIA. After single-cell RNA analysis, the relative percentages of CD14 monocytes were significantly greater in the JIA group. Cell-cell communication analysis revealed the significant role of the MIF signaling pathway in JIA.

Conclusion

In conclusion, this work describes the immune features of both the TCR and BCR repertoires under JIA conditions and provides novel insight into immunotherapy for JIA.

Analysis of the effects of M2 macrophage-derived PDE4C on the prognosis, metastasis and immunotherapy benefit of osteosarcoma

Tumour-associated macrophages (TAMs), encompassing M1 and M2 subtypes, exert significant effects on osteosarcoma (OS) progression and immunosuppression. However, the impacts of TAM-derived biomarkers on the progression of OS remains limited. The GSE162454 profile was subjected to single-cell RNA (scRNA) sequencing analysis to identify crucial mediators between TAMs and OS cells. The clinical features, effects and mechanisms of these mediators on OS cells and tumour microenvironment were evaluated via biological function experiments and molecular biology experiments. Phosphodiesterase 4C (PDE4C) was identified as a pivotal mediator in the communication between M2 macrophages and OS cells. Elevated levels of PDE4C were detected in OS tissues, concomitant with M2 macrophage level, unfavourable prognosis and metastasis. The expression of PDE4C was observed to increase during the conversion process of THP-1 cells to M2 macrophages, which transferred the PDE4C mRNA to OS cells through exosome approach. PDE4C increased OS cell proliferation and mobility via upregulating the expression of collagens. Furthermore, a positive correlation was observed between elevated levels of PDE4C and increased TIDE score, decreased response rate following immune checkpoint therapy, reduced TMB and diminished PDL1 expression. Collectively, PDE4C derived from M2 macrophages has the potential to enhance the proliferation and mobility of OS cells by augmenting collagen expression. PDE4C may serve as a valuable biomarker for prognosticating patient outcomes and response rates following immunotherapy.

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.

METHODS

The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.

RESULTS

Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.

CONCLUSION

Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Seq-Scope Protocol: Repurposing Illumina Sequencing Flow Cells for High-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) technologies represent a significant advance in gene expression studies, aiming to profile the entire transcriptome from a single histological slide. These techniques are designed to overcome the constraints faced by traditional methods such as immunostaining and RNA in situ hybridization, which are capable of analyzing only a few target genes simultaneously. However, the application of ST in histopathological analysis is also limited by several factors, including low resolution, a limited range of genes, scalability issues, high cost, and the need for sophisticated equipment and complex methodologies. Seq-Scope-a recently developed novel technology-repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, thereby overcoming these limitations. Here we provide a detailed step-by-step protocol to implement Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell that allows for the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. In addition to detailing how to prepare a frozen tissue section for both histological imaging and sequencing library preparation, we provide comprehensive instructions and a streamlined computational pipeline to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.

Self-assembly of differentiated dental pulp stem cells facilitates spheroid human dental organoid formation and prevascularization

BACKGROUND

The self-assembly of solid organs from stem cells has the potential to greatly expand the applicability of regenerative medicine. Stem cells can self-organise into microsized organ units, partially modelling tissue function and regeneration. Dental pulp organoids have been used to recapitulate the processes of tooth development and related diseases. However, the lack of vasculature limits the utility of dental pulp organoids.

AIM

To improve survival and aid in recovery after stem cell transplantation, we demonstrated the three-dimensional (3D) self-assembly of adult stem cell-human dental pulp stem cells (hDPSCs) and endothelial cells (ECs) into a novel type of spheroid-shaped dental pulp organoid in vitro under hypoxia and conditioned medium (CM).

METHODS

During culture, primary hDPSCs were induced to differentiate into ECs by exposing them to a hypoxic environment and CM. The hypoxic pretreated hDPSCs were then mixed with ECs at specific ratios and conditioned in a 3D environment to produce prevascularized dental pulp organoids. The biological characteristics of the organoids were analysed, and the regulatory pathways associated with angiogenesis were studied.

RESULTS

The combination of these two agents resulted in prevascularized human dental pulp organoids (Vorganoids) that more closely resembled dental pulp tissue in terms of morphology and function. Single-cell RNA sequencing of dental pulp tissue and RNA sequencing of Vorganoids were integrated to analyse key regulatory pathways associated with angiogenesis. The biomarkers forkhead box protein O1 and fibroblast growth factor 2 were identified to be involved in the regulation of Vorganoids.

CONCLUSION

In this innovative study, we effectively established an in vitro model of Vorganoids and used it to elucidate new mechanisms of angiogenesis during regeneration, facilitating the development of clinical treatment strategies.

Bulk anda single-cell transcriptome profiling reveals the molecular characteristics of T cell-mediated tumor killing in pancreatic cancer

Background

Despite the potential of immune checkpoint blockade (ICB) as a promising treatment for Pancreatic adenocarcinoma (PAAD), there is still a need to identify specific subgroups of PAAD patients who may benefit more from ICB. T cell-mediated tumor killing (TTK) is the primary concept behind ICB. We explored subtypes according to genes correlated with the sensitivity to TKK and unraveled their underlying associations for PAAD immunotherapies.

Methods

Genes that control the responsiveness of T cell-induced tumor destruction (GSTTK) were examined in PAAD, focusing on their varying expression levels and association with survival results. Moreover, samples with PAAD were separated into two subsets using unsupervised clustering based on GSTTK. Variability was evident in the tumor immune microenvironment, genetic mutation, and response to immunotherapy among different groups. In the end, we developed TRGscore, an innovative scoring system, and investigated its clinical and predictive significance in determining sensitivity to immunotherapy.

Results

Patients with PAAD were categorized into 2 clusters based on the expression of 52 GSTTKs, which showed varying levels and prognostic relevance, revealing unique TTK patterns. Survival outcome, immune cell infiltration, immunotherapy responses, and functional enrichment are also distinguished among the two clusters. Moreover, we found the CATSPER1 gene promotes the progression of PAAD through experiments. In addition, the TRGscore effectively predicted the responses to chemotherapeutics or immunotherapy in patients with PAAD and overall survival.

Conclusions

TTK exerted a vital influence on the tumor immune environment in PAAD. A greater understanding of TIME characteristics was gained through the evaluation of the variations in TTK modes across different tumor types. It highlights variations in the performance of T cells in PAAD and provides direction for improved treatment approaches.

Application of Single Cell Gene Expression Technologies to Neurotoxicology

Neurotoxicological research faces the challenge of linking biological changes resulting from exposures to neuronal function. An additional challenge is understanding cell-type specific differences and selective vulnerabilities of distinct neuronal populations to toxic insults. Single cell RNA-sequencing (scRNA-seq) allows for measurement of the transcriptome of individual cells. This makes it a valuable tool for validating and characterizing cell types present in multicell type samples in complex tissue or cell culture models, but also for understanding how different cell types respond to toxic insults. Pathway analysis of differentially expressed genes can provide in depth insights into underlying cell type-specific mechanisms of neurotoxicity. Toxicological data often has to be translated to outcomes for human health which requires an understanding of inter-species differences. Transcriptomic data aids in understanding these differences, including understanding developmental timelines of different species. We believe that scRNA-seq holds exciting promises for future neurotoxicological research.

A novel EIF3C-related CD8+ T-cell signature in predicting prognosis and immunotherapy response of nasopharyngeal carcinoma

PURPOSE

At present, dysfunctional CD8+ T-cells in the nasopharyngeal carcinoma (NPC) tumor immune microenvironment (TIME) have caused unsatisfactory immunotherapeutic effects, such as a low response rate of anti-PD-L1 therapy. Therefore, there is an urgent need to identify reliable markers capable of accurately predicting immunotherapy efficacy.

METHODS

Utilizing various algorithms for immune-infiltration evaluation, we explored the role of EIF3C in the TIME. We next found the influence of EIF3C expression on NPC based on functional analyses and RNA sequencing. By performing correlation and univariate Cox analyses of CD8+ Tcell markers from scRNA-seq data, we identified four signatures, which were then used in conjunction with the lasso algorithm to determine corresponding coefficients in the resulting EIF3C-related CD8+ T-cell signature (ETS). We subsequently evaluated the prognostic value of ETS using univariate and multivariate Cox regression analyses, Kaplan-Meier curves, and the area under the receiver operating characteristic curve (AUROC).

RESULTS

Our results demonstrate a significant relationship between low expression of EIF3C and high levels of CD8+ T-cell infiltration in the TIME, as well as a correlation between EIF3C expression and progression of NPC. Based on the expression levels of four EIF3C-related CD8+ T-cell marker genes, we constructed the ETS predictive model for NPC prognosis, which demonstrated success in validation. Notably, our model can also serve as an accurate indicator for detecting immunotherapy response.

CONCLUSION

Our findings suggest that EIF3C plays a significant role in NPC progression and immune modulation, particularly in CD8+ T-cell infiltration. Furthermore, the ETS model holds promise as both a prognostic predictor for NPC patients and a tool for adjusting individualized immunotherapy strategies.

Molecular map of disulfidptosis-related genes in lung adenocarcinoma: the perspective toward immune microenvironment and prognosis

BACKGROUND

Disulfidptosis is a recently discovered form of programmed cell death that could impact cancer development. Nevertheless, the prognostic significance of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) requires further clarification.

METHODS

This study systematically explores the genetic and transcriptional variability, prognostic relevance, and expression profiles of DRGs. Clusters related to disulfidptosis were identified through consensus clustering. We used single-sample gene set enrichment analysis and ESTIMATE to assess the tumor microenvironment (TME) in different subgroups. We conducted a functional analysis of differentially expressed genes between subgroups, which involved gene ontology, the Kyoto encyclopedia of genes and genomes, and gene set variation analysis, in order to elucidate their functional status. Prognostic risk models were developed using univariate Cox regression and the least absolute shrinkage and selection operator regression. Additionally, single-cell clustering and cell communication analysis were conducted to enhance the understanding of the importance of signature genes. Lastly, qRT-PCR was employed to validate the prognostic model.

RESULTS

Two clearly defined DRG clusters were identified through a consensus-based, unsupervised clustering analysis. Observations were made concerning the correlation between changes in multilayer DRG and various clinical characteristics, prognosis, and the infiltration of TME cells. A well-executed risk assessment model, known as the DRG score, was developed to predict the prognosis of LUAD patients. A high DRG score indicates increased TME cell infiltration, a higher mutation burden, elevated TME scores, and a poorer prognosis. Additionally, the DRG score showed a significant correlation with the tumor mutation burden score and the tumor immune dysfunction and exclusion score. Subsequently, a nomogram was established for facilitating the clinical application of the DRG score, showing good predictive ability and calibration. Additionally, crucial DRGs were further validated by single-cell sequencing data. Finally, crucial DRGs were further validated by qRT-PCR and immunohistochemistry.

CONCLUSION

Our new DRG signature risk score can predict the immune landscape and prognosis of LUAD. It also serves as a reference for LUAD's immunotherapy and chemotherapy.

Single-cell transcriptomics analysis of zebrafish brain reveals adverse effects of manganese on neurogenesis

Manganese (Mn) is considered as an important environmental risk factor for Parkinson's disease. Excessive exposure to Mn can damage various neural cells and affect the neurogenesis, resulting in neurological dysfunction. However, the specific mechanisms of Mn exposure affecting neurogenesis have not been well understood, including compositional changes and heterogeneity of various neural cells. Zebrafish have been successfully used as a neurotoxicity model due to its homology with mammals in several key regions of the brain, as well as its advantages such as small size. We performed single-cell RNA sequencing of zebrafish brains from normal and Mn-exposed groups. Our results suggested that low levels of Mn exposure activated neurogenesis in the zebrafish brain, including promoting the proliferation of neural progenitor cells and differentiation to newborn neurons and oligodendrocytes, while high levels of Mn exposure inhibited neurogenesis and neural function. Mn could affect neurogenesis through specific molecular pathways. In addition, Mn regulated intercellular communication and affected cellular communication in neural cells through specific signaling pathways. Taken together, our study elucidates the cellular composition of the zebrafish brain and adds to the understanding of the mechanisms involved in Mn-induced neurogenesis damage.

Cytogenetics in the management of hematological malignancies: An overview of alternative technologies for cytogenetic characterization

Genomic characterization is an essential part of the clinical management of hematological malignancies for diagnostic, prognostic and therapeutic purposes. Although CBA and FISH are still the gold standard in hematology for the detection of CNA and SV, some alternative technologies are intended to complement their deficiencies or even replace them in the more or less near future. In this article, we provide a technological overview of these alternatives. CMA is the historical and well established technique for the high-resolution detection of CNA. For SV detection, there are emerging techniques based on the study of chromatin conformation and more established ones such as RTMLPA for the detection of fusion transcripts and RNA-seq to reveal the molecular consequences of SV. Comprehensive techniques that detect both CNA and SV are the most interesting because they provide all the information in a single examination. Among these, OGM is a promising emerging higher-solution technique that offers a complete solution at a contained cost, at the expense of a relatively low throughput per machine. WGS remains the most adaptable solution, with long-read approaches enabling very high-resolution detection of CAs, but requiring a heavy bioinformatics installation and at a still high cost. However, the development of high-resolution genome-wide detection techniques for CAs allows for a much better description of chromoanagenesis. Therefore, we have included in this review an update on the various existing mechanisms and their consequences and implications, especially prognostic, in hematological malignancies.

Heterogeneity and synaptic plasticity analysis of hippocampus based on db-/- mice induced diabetic encephalopathy

Chronic hyperglycemia can cause changes in synaptic plasticity of hippocampal cells, which has accelerated the pathological process of cognitive dysfunction. However, the heterogeneity of the hippocampal cell populations under long term high glucose statement remains largely unknown. To mimic chronic hyperglycemia induced cognitive function deficit in vivo, db-/- diabetic mice was selected and Novel Object Recognition(NOR) behavior tests were performed. Based on diabetic induced cognitive impairment(CI) animal model, single-cell RNA sequencing was performed in the hippocampus of CI group (21,379 cells) or control group (20,045 cells), and single cell RNA sequencing was applied, and then the single cell atlas of gene expression was profiled. The comprehensive analysis explicated 18 nerve cell clusters, including 9 distinct sub-clusters, More in-depth analysis of oligodendrocyte precursor cells(OPCs) showed five distinct OPCs sub-clusters including expressing marker gene Lingo2-OPCs, Kcnc1-OPCs, Sst-OPCs, Slc6a1-OPCs and Lhfpl3-OPCs, which seems to be able to proliferate, migrate, and finally differentiate into mature oligodendrocytes and produce myelin. To be noted, differentially expressed genes(DEGs) of the Sst-OPCs sub-cluster indicated that the genes participating in neuroactive ligand-receptor interaction, nervous system development and inflammatory process were up-regulated in diabetic induced cognitive impairment(DCI) groups compared to normal control groups. Integrating the data of neuroplasticity regulation, the 20th top-enriched biological process was associated with neuroplasticity regulation in CI groups compared to control groups. Among these neuroplasticity-related genes, the intersectional gene Sstr2 may play an important role in neuroplasticity regulation. Focused on neuroplasticity regulation and its related specific genes may provide potential new clues for the treatment of diabetes mellitus complicated with cognitive impairment. In summary, we showed the comprehensively transcriptional landscape of hippocampal cells in the db-/- diabetic mice with cognitive dysfunction, distinctive cell sub-clusters and the gene expression characteristics were identified, and also their special functions were proposed, which may give new clues and potential targets for diagnosis and treatment of diabetic encephalopathy.

The hypothalamic-pituitary-thyroid axis is intact in male insulin receptor substrate 4 knockout mice

OBJECTIVE

Loss of function mutations in the insulin receptor substrate 4 (IRS4) gene cause a rare form of X-linked congenital central hypothyroidism in boys and men. Affected individuals show decreased thyroid-stimulation hormone (TSH) secretion. Members of the IRS family canonically act as scaffold proteins between tyrosine kinase receptors and downstream effectors. How loss of IRS4 affects TSH synthesis or secretion is unresolved. We therefore assessed IRS4's role in the hypothalamic-pituitary-thyroid axis of Irs4 knockout mice.

METHODS

We generated two global Irs4 knockout mouse lines harboring either two or four base-pair deletions that result in frameshifts and loss of most of the IRS4 protein.

RESULTS

Under normal laboratory conditions, Irs4 knockout males did not exhibit impairments in pituitary expression of TSH subunit genes (Tshb or Cga) or in the thyrotropin-releasing hormone (TRH) receptor. Additionally, their serum thyroid hormone, T3 (triiodothyronine) and T4 (thyroxine), and hypothalamic Trh expression levels were normal. When Irs4 knockouts were rendered hypothyroid with a low-iodine diet supplemented with propylthiouracil (PTU) for 3 weeks, their serum TSH increased similarly to wild-type males.

CONCLUSIONS

Overall, Irs4 knockout mice do not exhibit central hypothyroidism or otherwise appear to phenocopy IRS4 deficient patients. Compensation by another IRS protein may explain euthyroidism in these animals.

Technologies to Study Genetics and Molecular Pathways

Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.

Modeling human immune responses to vaccination in vitro

The human immune system is a complex network of coordinated components that are crucial for health and disease. Animal models, commonly used to study immunomodulatory agents, are limited by species-specific differences, low throughput, and ethical concerns. In contrast, in vitro modeling of human immune responses can enable species- and population-specific mechanistic studies and translational development within the same study participant. Translational accuracy of in vitro models is enhanced by accounting for genetic, epigenetic, and demographic features such as age, sex, and comorbidity. This review explores various human in vitro immune models, considers evidence that they may resemble human in vivo responses, and assesses their potential to accelerate and de-risk vaccine discovery and development.

Pulsed field ablation as a precise approach for cardiac arrhythmia treatment via cardiac microenvironment remodeling

PFA uses short-duration, high-voltage electrical pulses to induce transient or irreversible electroporation on cell membranes, causing cell death. Selective inhibition of chaotic electrical signals in morbid cardiomyocytes significantly aids the treatment of atrial fibrillation, ventricular tachycardia, and other heart arrhythmias. Recent preclinical and clinical studies have only investigated physical changes, such as lesion size and myocardial scar. Compared to radiofrequency ablation and cryoballoon ablation, PFA causes less postoperative myocardial cell fibrosis and inflammatory reaction and does not result in myocardial necrosis or tissue scar formation. However, the regulatory mechanism of cellular stress following PFA treatment remains unknown. This study aimed to analyze the transcriptome of the mouse ventricle after PFA treatment. The animals were subjected to a 225-V electric pulse with a 1.5-mm gap between the positive and negative electrodes. Hearts were harvested at 3, 6, 12, 24 h, and 2, 5 days for myocardial zymogram testing. PFA-treated ventricular regions were selected for single-nucleus sequencing. We discovered that PFA remodeled the cardiac microenvironment as a whole. Further, we discussed the possible stress response and wound-healing mechanism in non-targeted cells. In conclusion, PFA allowed effective and selective ventricular myocardium ablation with controllable inflammation.

Predicting drug response from single-cell expression profiles of tumours

BACKGROUND

Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.

RESULTS

Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.

CONCLUSIONS

DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

Defining the molecular fingerprint of bladder and kidney fibroblasts

Fibroblasts are integral to the organization and function of all organs and play critical roles in pathologies such as fibrosis; however, we have limited understanding of the fibroblasts that populate the bladder and kidney. In this review, I describe how transcriptomics is leading to a revolution in our understanding of fibroblast biology by defining the molecular fingerprint (i.e., transcriptome) of universal and specialized fibroblast types, revealing gene signatures that allows one to resolve fibroblasts from other mesenchymal cell types, and providing a new comprehension of the fibroblast lineage. In the kidney, transcriptomics is giving us new insights into the molecular fingerprint of kidney fibroblasts, including those for cortical fibroblasts, medullary fibroblasts, and erythropoietin (EPO)-producing Norn fibroblasts, as well as new information about the gene signatures of kidney myofibroblasts and the transition of kidney fibroblasts into myofibroblasts. Transcriptomics has also revealed that the major cell type in the bladder interstitium is the fibroblast, and that multiple fibroblast types, each with their own molecular fingerprint, are found in the bladder wall. Interleaved throughout is a discussion of how transcriptomics can drive our future understanding of fibroblast identification, diversity, function, and their roles in bladder and kidney biology and physiology in health and in disease states.

Identification of potential immunologic resilience in the healing process of diabetic foot ulcers

Diabetic foot ulcers (DFUs) are one of the most common and challenging complications of diabetes, yet our understanding of their pathogenesis remains limited. We collected gene expression data of DFU patients from public databases. Bioinformatics tools were applied for systematic analysis, including the identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA) and enrichment analysis. We further used single-cell RNA sequencing to identify the distribution of different cell populations in DFU. Finally, key results were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and flow cytometry. We identified 217 DEGs between ulcerated and healthy skin, and 37 DEGs between healing ulcers and ulcers. WGCNA revealed that the cyan module had the highest positive correlation with healthy skin and negative correlation with ulcers. The black module had the highest negative correlation with healthy skin and positive correlation with ulcers. Enrichment analysis showed that the genes in the cyan module were mainly associated with complement and coagulation cascades, while the genes in the black module were mainly associated with the IL-17 signalling pathway. In addition, CD8 T cells were significantly lower in ulcers than in healthy and healing ulcers. By comparing marker genes of CD8 T cells, we identified key genes in the cyan and black modules and validated their expression using RT-qPCR. The proportion of CD8 T cells was increased in healing ulcers. Flow cytometry detected increased levels of CD8 T, B and natural killer cells in healing ulcers. CD8 T cells and related key genes play an important role in the healing process of DFU. The results of this study provide a new perspective for understanding the pathogenesis and treatment of DFU.

Machine Learning-Based Integration Develops a Macrophage-Related Index for Predicting Prognosis and Immunotherapy Response in Lung Adenocarcinoma

BACKGROUND

Macrophages play a critical role in tumor immune microenvironment (TIME) formation and cancer progression in lung adenocarcinoma (LUAD). However, few studies have comprehensively and systematically described the characteristics of macrophages in LUAD.

METHODS

This study identified macrophage-related markers with single-cell RNA sequencing data from the GSE189487 dataset. An integrative machine learning-based procedure based on 10 algorithms was developed to construct a macrophage-related index (MRI) in The Cancer Genome Atlas (TCGA), GSE30219, GSE31210, and GSE72094 datasets. Several algorithms were used to evaluate the associations of MRI with TIME and immunotherapy-related biomarkers. The role of MRI in predicting the immunotherapy response was evaluated with the GSE91061 dataset.

RESULTS

The optimal MRI constructed by the combination of the Lasso algorithm and plsRCox was an independent risk factor in LUAD and showed a stable and powerful performance in predicting the overall survival rate of patients with LUAD. Those with low MRI scores had a higher TIME score, a higher level of immune cells, a higher immunophenoscore, and a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a better response to immunotherapy. The IC50 value of common drugs for chemotherapy and target therapy with low MRI scores was higher compared to high MRI scores. Moreover, the survival prediction nomogram, developed from MRI, had good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of LUAD.

CONCLUSION

Our study constructed for the first time a consensus MRI for LUAD with 10 machine learning algorithms. The MRI could be helpful for risk stratification, prognosis, and selection of treatment approach in LUAD.

SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data

Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single-cell (sc) resolution are still open challenges in the biomedical field. The existence of countless bioinformatics tools, for the different analytical steps, increases the complexity of data interpretation and the difficulty to derive biological insights. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) datasets, enabling either independent or integrative analysis of the two modalities. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to the identification of distinct cell populations and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction. SCALA is fully parameterizable, presenting data in tabular format and producing publication-ready visualizations. The different available analysis modules can aid biomedical researchers in exploring, analyzing, and visualizing their data without any prior experience in coding. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling both scRNA-seq and scATAC-seq datasets. SCALA is developed in R, Shiny and JavaScript and is mainly available as a standalone version, while an online service of more limited capacity can be found at http://scala.pavlopouloslab.info or https://scala.fleming.gr.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Fibroblasts - the cellular choreographers of wound healing

Injuries to our skin trigger a cascade of spatially- and temporally-synchronized healing processes. During such endogenous wound repair, the role of fibroblasts is multifaceted, ranging from the activation and recruitment of innate immune cells through the synthesis and deposition of scar tissue to the conveyor belt-like transport of fascial connective tissue into wounds. A comprehensive understanding of fibroblast diversity and versatility in the healing machinery may help to decipher wound pathologies whilst laying the foundation for novel treatment modalities. In this review, we portray the diversity of fibroblasts and delineate their unique wound healing functions. In addition, we discuss future directions through a clinical-translational lens.

Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs

Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. These methods were developed for UMI-based sequencing protocols, where unique molecular identifiers (UMIs) help to remove PCR amplification noise by keeping track of the original molecules. In contrast, full-length protocols such as Smart-seq2 lack UMIs and retain amplification noise, making negative binomial models inapplicable. Here, we extend Pearson residuals to such read count data by modeling them as a compound process: we assume that the captured RNA molecules follow the negative binomial distribution, but are replicated according to an amplification distribution. Based on this model, we introduce compound Pearson residuals and show that they can be analytically obtained without explicit knowledge of the amplification distribution. Further, we demonstrate that compound Pearson residuals lead to a biologically meaningful gene selection and low-dimensional embeddings of complex Smart-seq2 datasets. Finally, we empirically study amplification distributions across several sequencing protocols, and suggest that they can be described by a broken power law. We show that the resulting compound distribution captures overdispersion and zero-inflation patterns characteristic of read count data. In summary, compound Pearson residuals provide an efficient and effective way to normalize read count data based on simple mechanistic assumptions.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for acute myeloid leukemia: a comprehensive single-cell RNA sequencing analysis

Acute myeloid leukemia (AML) is a complex mixed entity composed of malignant tumor cells, immune cells and stromal cells, with intra-tumor and inter-tumor heterogeneity. Single-cell RNA sequencing enables a comprehensive study of the highly complex tumor microenvironment, which is conducive to exploring the evolutionary trajectory of tumor cells. Herein, we carried out comprehensive analyses of aggrephagy-related cell clusters based on single-cell sequencing for patients with acute myeloid leukemia. A total of 11 specific cell types (T, NK, CMP, Myeloid, GMP, MEP, Promono, Plasma, HSC, B, and Erythroid cells) using t-SNE dimension reduction analysis. Several aggrephagy-related genes were highly expressed in the 11 specific cell types. Using Monocle analysis and NMF clustering analysis, six aggrephagy-related CD8+ T clusters, six aggrephagy-related NK clusters, and six aggrephagy-related Mac clusters were identified. We also evaluated the ligand-receptor links and Cell-cell communication using CellChat package and CellChatDB database. Furthermore, the transcription factors (TFs) of aggrephagy-mediated cell clusters for AML were assessed through pySCENIC package. Prognostic analysis of the aggrephagy-related cell clusters based on R package revealed the differences in prognosis of aggrephagy-mediated cell clusters. Immunotherapy of the aggrephagy-related cell clusters was investigated using TIDE algorithm and public immunotherapy cohorts. Our study revealed the significance of aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for AML.

The Use of Single-Cell RNA-Sequencing and Spatial Transcriptomics in Understanding the Pathogenesis and Treatment of Skin Diseases

The development of multiomic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. In this paper, we review the recent biological insights gained from the use of scRNA-seq and ST and the advantages of combining both for profiling skin diseases, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST in improving skin disease treatments and moving toward the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.

The Regulation Network of Glycerolipid Metabolism as Coregulators of Immunotherapy-Related Myocarditis

Background

To date, immunotherapy for patients with malignant tumors has shown a significant association with myocarditis. However, the mechanism of metabolic reprogramming changes for immunotherapy-related cardiotoxicity is still not well understood.

Methods

The CD45⁺ single-cell RNA sequencing (scRNA-seq) of the Pdcd1^-/-Ctla4^+/- and wild-type mouse heart in GSE213486 was downloaded to demonstrate the heterogeneity of immunocyte atlas in immunotherapy-related myocarditis. The liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectrum metabolomics analysis detects the metabolic network differences. The drug prediction, organelle level interaction, mitochondrial level regulatory network, and phosphorylation site prediction for key regulators have also been screened via multibioinformatics analysis methods.

Results

The scRNA analysis shows that the T cell is the main regulatory cell subpopulation in the pathological progress of immunotherapy-related myocarditis. Mitochondrial regulation pathway significantly participated in pseudotime trajectory- (PTT-) related differential expressed genes (DEGs) in the T cell subpopulation. Additionally, both the gene set enrichment analysis (GSEA) of PTT-related DEGs and LC-MS/MS metabolomics analysis showed that mitochondrial-regulated glycerolipid metabolism plays a central role in metabolic reprogramming changes for immunotherapy-related cardiotoxicity. Finally, the hub-regulated protease of diacylglycerol kinase zeta (Dgkz) was significantly identified and widely played various roles in glycerolipid metabolism, oxidative phosphorylation, and lipid kinase activation.

Conclusion

Mitochondrial-regulated glycerolipid metabolism, especially the DGKZ protein, plays a key role in the metabolic reprogramming of immunotherapy-related myocarditis.

Optimization of method for achieving a single-cell suspension from mouse corneas

The single-cell RNA-sequencing (scRNA-seq) technique is used to explore the biological characteristics of tissues under pathological and physiological conditions that include certain chronic eye diseases. Harvesting of single-cell suspensions is one challenge inherent to scRNA-seq procedures. This study aimed to use an optimized method to digest a whole mouse cornea to harvest single-cell suspensions. We utilized five different mouse cornea digestion methods to obtain single-cell suspensions: (1) 5 dissected mouse corneas were cut into pieces (∼0.5 mm) and digested in trypsin for 10 min, and this digestion was repeated for 10 cycles; (2) 5 dissected mouse corneas were cut into pieces and incubated with 5 mg/ml collagenase A at 37 °C for 1h and then further digested in trypsin at 37 °C for 10 min; (3) used the same approach as that used in method 2, but the second digestion step was performed in TrypLE for 20 min; (4) used the same approach as that used in method 2, but the concentration of collagenase A was 2 mg/ml and the incubation time was 2h; (5) used the same approach as that used in method 3, but the corneas were incubated in 2 mg/ml collagenase A at 37 °C for 2h. Trypan blue staining was used to calculate the cell viability and agglomeration rate. The cell types and percentages were determined using immunofluorescence staining. RNA integrity number (RIN) was measured by Agilent 2100. Method 1 showed the lowest cell yield (0.375 × 10⁵), epithelial cell percentage, and less than 70% cell viability, thus not a proper protocol. Method 2 showed the highest cell viability (over 90%), percentage of single-cell (89.53%), and high cell quantity (1.05 × 10⁵). Method 3 had a significantly lower cell viability (55.30%). Cell agglomeration rates of method 4 and 5 reached up to 20% and 13%, and with lower cell viability (72.51%, 59.87%, respectively) and decreased epithelial cell rate compared to method 2 and 3. The results suggest that method 2 (5 mg/ml collagenase A and trypsin) is a preferred protocol for digesting mouse cornea to obtain single-cell suspension which achieves the criterion of single-cell RNA sequencing.

Integrated single-cell and bulk characterization of cuproptosis key regulator PDHB and association with tumor microenvironment infiltration in clear cell renal cell carcinoma

Background

Renal clear cell carcinoma (ccRCC) is one of the most prevalent cancers worldwide. Accumulating evidence revealed that copper-induced cell death played a vital role in various tumors. However, the underlying mechanism of cuproptosis with molecular heterogeneity and tumor microenvironment (TME) in ccRCC remains to be elucidated. The present study aimed to discover the biological function of cuproptosis regulators with the potential to guide clinical therapy.

Methods

Using Single-cell RNA-seq, bulk transcriptome and other multi-omics datasets, we identify essential cuproptosis-related hub gene PDHB for further study. The dysregulation of PDHB in ccRCC was characterized, together with survival outcomes, pathway enrichment and immune infiltration among tumor microenvironments. The functional significance and clinical association of PDHB was validated with loss of function experiments and surgical removal specimens.

Results

PDHB mRNA and protein expression level was significantly downregulated in ccRCC tissues compared with normal and paired normal tissues. Clinicopathological parameters and tissue microarray (TMA) indicated that PDHB was identified as a prognostic factor for survival outcomes among ccRCC patients. Additionally, low PDHB was negatively correlated with Treg cells, indicating an immunosuppressive microenvironment. Mechanistically, knockdown PDHB appeared to promote the RCC cells proliferation, migration, and invasion potentials. Subsequent studies showed that copper-induced cell death activation could overcome sunitinib resistance in RCC cells.

Conclusion

This research illustrated a cuproptosis-related hub gene PDHB which could serve as a potential prognostic marker and provide therapeutic benefits for clinical treatment of ccRCC patients.

Single-cell RNA sequencing in skeletal muscle developmental biology

Skeletal muscle is the most extensive tissue in mammals, and they perform several functions; it is derived from paraxial mesodermal somites and undergoes hyperplasia and hypertrophy to form multinucleated, contractile, and functional muscle fibers. Skeletal muscle is a complex heterogeneous tissue composed of various cell types that establish communication strategies to exchange biological information; therefore, characterizing the cellular heterogeneity and transcriptional signatures of skeletal muscle is central to understanding its ontogeny's details. Studies of skeletal myogenesis have focused primarily on myogenic cells' proliferation, differentiation, migration, and fusion and ignored the intricate network of cells with specific biological functions. The rapid development of single-cell sequencing technology has recently enabled the exploration of skeletal muscle cell types and molecular events during development. This review summarizes the progress in single-cell RNA sequencing and its applications in skeletal myogenesis, which will provide insights into skeletal muscle pathophysiology.

Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte signature in SKCM

Background

Global patterns of immune cell communications in the immune microenvironment of skin cutaneous melanoma (SKCM) haven't been well understood. Here we recognized signaling roles of immune cell populations and main contributive signals. We explored how multiple immune cells and signal paths coordinate with each other and established a prognosis signature based on the key specific biomarkers with cellular communication.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset was downloaded from the Gene Expression Omnibus (GEO) database, in which various immune cells were extracted and re-annotated according to cell markers defined in the original study to identify their specific signs. We computed immune-cell communication networks by calculating the linking number or summarizing the communication probability to visualize the cross-talk tendency in different immune cells. Combining abundant analyses of communication networks and identifications of communication modes, all networks were quantitatively characterized and compared. Based on the bulk RNA sequencing data, we trained specific markers of hub communication cells through integration programs of machine learning to develop new immune-related prognostic combinations.

Results

An eight-gene monocyte-related signature (MRS) has been built, confirmed as an independent risk factor for disease-specific survival (DSS). MRS has great predictive values in progression free survival (PFS) and possesses better accuracy than traditional clinical variables and molecular features. The low-risk group has better immune functions, infiltrated with more lymphocytes and M1 macrophages, with higher expressions of HLA, immune checkpoints, chemokines and costimulatory molecules. The pathway analysis based on seven databases confirms the biological uniqueness of the two risk groups. Additionally, the regulon activity profiles of 18 transcription factors highlight possible differential regulatory patterns between the two risk groups, suggesting epigenetic event-driven transcriptional networks may be an important distinction. MRS has been identified as a powerful tool to benefit SKCM patients. Moreover, the IFITM3 gene has been identified as the key gene, validated to express highly at the protein level via the immunohistochemical assay in SKCM.

Conclusion

MRS is accurate and specific in evaluating SKCM patients' clinical outcomes. IFITM3 is a potential biomarker. Moreover, they are promising to improve the prognosis of SKCM patients.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

BACKGROUND

Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights.

RESULTS

Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files.

CONCLUSIONS

We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

Application of single cell multiomics points to changes in chromatin accessibility near calcitonin receptor like receptor and a possible role for adrenomedullin in the post-shock lung

Introduction

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a commonly occurring sequelae of traumatic injury resulting from indirect insults like hypovolemic shock and/or extrapulmonary sepsis. The high lethality rate associated with these pathologies outlines the importance of clarifying the "priming" effects seen in the post-shock lung microenvironment, which are understood to bring about a dysregulated or overt immune response when triggered by a secondary systemic infectious/septic challenge culminating in ALI. In this pilot project, we test the hypothesis that application of a single cell multiomics approach can elucidate novel phenotype specific pathways potentially contributing to shock-induced ALI/ARDS.

Methods

Hypovolemic shock was induced in C57BL/6 (wild-type), PD-1, PD-L1, or VISTA gene deficient male mice, 8-12 weeks old. Wild-type sham surgeries function as negative controls. A total of 24-h post-shock rodents were sacrificed, their lungs harvested and sectioned, with pools prepared from 2 mice per background, and flash frozen on liquid nitrogen. N = 2 biological replicates (representing 4 mice total) were achieved for all treatment groups across genetic backgrounds. Samples were received by the Boas Center for Genomics and Human Genetics, where single cell multiomics libraries were prepared for RNA/ATAC sequencing. The analysis pipeline Cell Ranger ARC was implemented to attain feature linkage assessments across genes of interest.

Results

Sham (pre-shock) results suggest high chromatin accessibility around calcitonin receptor like receptor (CALCRL) across cellular phenotypes with 17 and 18 feature links, exhibiting positive correlation with gene expression between biological replicates. Similarity between both sample chromatin profiles/linkage arcs is evident. Post-shock wild-type accessibility is starkly reduced across replicates where the number of feature links drops to 1 and 3, again presenting similar replicate profiles. Samples from shocked gene deficient backgrounds displayed high accessibility and similar profiles to the pre-shock lung microenvironment.

Conclusion

High pre-shock availability of DNA segments and their positive correlation with CALCRL gene expression suggests an apparent regulatory capacity on transcription. Post-shock gene deficient chromatin profiles presented similar results to that of pre-shock wild-type samples, suggesting an influence on CALCRL accessibility. Key changes illustrated in the pre-ALI context of shock may allow for additional resolution of "priming" and "cellular pre-activation/pre-disposition" processes within the lung microenvironment.

Cytohesin-4 Upregulation in Glioma-Associated M2 Macrophages Is Correlated with Pyroptosis and Poor Prognosis

Cytohesin-4 (CYTH4) is a member of the PSCD family. Members of this family appear to mediate the regulation of protein sorting and membrane trafficking. In previous studies, CYTH4 has been linked with multiple brain diseases, but not glioma, the most common type of brain tumor. We utilized multiple glioma single-cell RNA sequencing datasets and bulk data from the TCGA and CGGA and conducted GSEA and KEGG and GO analyses. Biomarker potential was tested via ROC curve analysis. Radar plots were used to study TMB and MSI correlations. Immune cell studies were conducted using CIBERSORT. All statistical analyses were performed in R software and GraphPad Prism 9. CYTH4 was overexpressed in the glioma macrophage population in several single-cell RNA sequencing datasets and was most correlated with M2 macrophages. CYTH4 expression was higher in tumor tissues and was correlated with survival and WHO grade. ROC curves suggested CYTH4 overexpression to be a potential glioma biomarker. GSEA results indicated a relationship between CYTH4 and apoptosis, and PPI analysis supported a pyroptosis correlation. KEGG and GO analysis results linked CYTH4 with antigen processing and presentation and neutrophil activities. In summary, the study identified a CYTH4/pyroptosis/M2 macrophage axis. CYTH4 was upregulated in M2 macrophages in glioma and affected pyroptosis. CYTH4 overexpression is a potential biomarker predicting a poor prognosis.

Identification and validation of a novel signature based on cell-cell communication in head and neck squamous cell carcinoma by integrated analysis of single-cell transcriptome and bulk RNA-sequencing

Background

The heterogeneous crosstalk between tumor cells and other cells in their microenvironment means a notable difference in clinical outcomes of head and neck squamous cell carcinoma (HNSCC). CD8+ T cells and macrophages are effector factors of the immune system, which have direct killing and phagocytosis effects on tumor cells. How the evolution of their role in the tumor microenvironment influences patients clinically remains a mystery. This study aims to investigate the complex communication networks in the HNSCC tumor immune microenvironment, elucidate the interactions between immune cells and tumors, and establish prognostic risk model.

Methods

20 HNSCC samples single-cell rna sequencing (scRNA-seq) data and bulk rna-seq data were derived from public databases. The "cellchat" R package was used to identify cell-to-cell communication networks and prognostic related genes, and then cell-cell communication (ccc) molecular subtypes were constructed by unsupervised clustering. Kaplan-Meier(K-M) survival analysis, clinical characteristics analysis, immune microenvironment analysis, immune cell infiltration analysis and CD8+T cell differentiation correlation analysis were performed. Finally, the ccc gene signature including APP, ALCAM, IL6, IL10 and CD6 was constructed based on univariate Cox analysis and multivariate Cox regression. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) analysis were used to evaluate the model in the train group and the validation group, respectively.

Results

With CD8+T cells from naive to exhaustion state, significantly decreased expression of protective factor (CD6 gene) is associated with poorer prognosis in patients with HNSCC. The role of macrophages in the tumor microenvironment has been identified as tumor-associated macrophage (TAM), which can promote tumor proliferation and help tumor cells provide more nutrients and channels to facilitate tumor cell invasion and metastasis. In addition, based on the strength of all ccc in the tumor microenvironment, we identified five prognostic ccc gene signatures (cccgs), which were identified as independent prognostic factors by univariate and multivariate analysis. The predictive power of cccgs was well demonstrated in different clinical groups in train and test cohorts.

Conclusion

Our study highlights the propensity for crosstalk between tumors and other cells and developed a novel signature on the basis of a strong association gene for cell communication that has a powerful ability to predict prognosis and immunotherapy response in patients with HNSCC. This may provide some guidance for developing diagnostic biomarkers for risk stratification and therapeutic targets for new therapeutic strategies.

Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review

Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.

Single-cell RNA-sequencing data analysis reveals a highly correlated triphasic transcriptional response to SARS-CoV-2 infection

Single-cell RNA sequencing (scRNA-seq) is currently one of the most powerful techniques available to study the transcriptional response of thousands of cells to an external perturbation. Here, we perform a pseudotime analysis of SARS-CoV-2 infection using publicly available scRNA-seq data from human bronchial epithelial cells and colon and ileum organoids. Our results reveal that, for most genes, the transcriptional response to SARS-CoV-2 infection follows a non-linear pattern characterized by an initial and a final down-regulatory phase separated by an intermediate up-regulatory stage. A correlation analysis of transcriptional profiles suggests a common mechanism regulating the mRNA levels of most genes. Interestingly, genes encoded in the mitochondria or involved in translation exhibited distinct pseudotime profiles. To explain our results, we propose a simple model where nuclear export inhibition of nsp1-sensitive transcripts will be sufficient to explain the transcriptional shutdown of SARS-CoV-2 infected cells.

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.

A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data

With the emergence of single-cell RNA sequencing (scRNA-seq) technology, scientists are able to examine gene expression at single-cell resolution. Analysis of scRNA-seq data has its own challenges, which stem from its high dimensionality. The method of machine learning comes with the potential of gene (feature) selection from the high-dimensional scRNA-seq data. Even though there exist multiple machine learning methods that appear to be suitable for feature selection, such as penalized regression, there is no rigorous comparison of their performances across data sets, where each poses its own challenges. Therefore, in this paper, we analyzed and compared multiple penalized regression methods for scRNA-seq data. Given the scRNA-seq data sets we analyzed, the results show that sparse group lasso (SGL) outperforms the other six methods (ridge, lasso, elastic net, drop lasso, group lasso, and big lasso) using the metrics area under the receiver operating curve (AUC) and computation time. Building on these findings, we proposed a new algorithm for feature selection using penalized regression methods. The proposed algorithm works by selecting a small subset of genes and applying SGL to select the differentially expressed genes in scRNA-seq data. By using hierarchical clustering to group genes, the proposed method bypasses the need for domain-specific knowledge for gene grouping information. In addition, the proposed algorithm provided consistently better AUC for the data sets used.

Fibronectin-1 is a dominant mechanism for rheumatoid arthritis via the mediation of synovial fibroblasts activity

Rheumatoid arthritis (RA) has a high incidence and adverse effects on patients, thus posing a serious threat to people’s life and health. However, the underlying mechanisms regarding the development of RA are still elusive. Herein, we aimed to evaluate the RA-associated molecular mechanisms using the scRNA-seq technique. We used the GEO database to obtain scRNA-seq datasets for synovial fibroblasts (SFs) from RA cases, and the genes were then analyzed using principal component analysis (PCA) and T-Stochastic Neighbor Embedding (TSNE) analyses. Bioinformatics evaluations were carried out for asserting the highly enriched signaling pathways linked to the marker genes, and the key genes related to RA initiation were further identified. According to the obtained results, 3 cell types (0, 1, and 2) were identified by TSNE and some marker genes were statistically upregulated in cell type 1 than the other cell types. These marker genes predominantly contributed to extracellular matrix (ECM) architecture, collagen-harboring ECM, and ECM structural components, and identified as enriched with PI3K/AKT signaling cascade. Notably, fibronectin-1 (FN-1) has been identified as a critical gene that is strongly linked to the development of SFs and has enormous promise for regulating the onset of RA. Moreover, such an investigation offers novel perspectives within onset/progression of RA, suggesting that FN-1 may be a key therapeutic target for RA therapies.

Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD

Despite the knowledge of the main mechanisms involved in facioscapulohumeral muscular dystrophy (FSHD), the high heterogeneity and variable penetrance of the disease complicate the diagnosis, characterization and genotype–phenotype correlation of patients and families, raising the need for further research and data. Thus, the present review provides an update of the main molecular aspects underlying the complex architecture of FSHD, including the genetic factors (related to D4Z4 repeated units and FSHD-associated genes), epigenetic elements (D4Z4 methylation status, non-coding RNAs and high-order chromatin interactions) and gene expression profiles (FSHD transcriptome signatures both at bulk tissue and single-cell level). In addition, the review will also describe the methods currently available for investigating the above-mentioned features and how the resulting data may be combined with artificial-intelligence-based pipelines, with the purpose of developing a multifunctional tool tailored to enhancing the knowledge of disease pathophysiology and progression and fostering the research for novel treatment strategies, as well as clinically useful biomarkers. In conclusion, the present review highlights how FSHD should be regarded as a disease characterized by a molecular spectrum of genetic and epigenetic factors, whose alteration plays a differential role in DUX4 repression and, subsequently, contributes to determining the FSHD phenotype.

Pediatric Sarcomas: The Next Generation of Molecular Studies

Simple Summary

There has been an incredible amount of discovery in pediatric sarcomas, but much remains to be accomplished. Clinical challenges include diagnostic heterogeneity and the poor outcome of patients with high risk, metastatic, and relapsed disease. The emergence of single cell sequencing has allowed the ability to document tumor cell heterogeneity in amazing detail, but it does not allow the ability to visualize spatial orientation. This problem has been solved by spatial multi-omics, which can be used to map tumors and visualize the distribution of critical transcripts, mutations, and proteins. However, these tools only offer observational data. High-throughput functional genomics provides a powerful way to highlight oncogenic drivers and potential therapy opportunities. Research has been hamstrung by a need for annotated specimens, particularly in post-therapy, relapsed, and metastatic disease, and initial biopsies offer only limited data opportunities. Data complexity, variability, and inconsistency present problems best approached with AI/machine learning. We stand on the threshold of a revolution in cancer cell biology that has the potential for translation into more effective and more directed therapies, particularly for previously recalcitrant diseases.

Abstract

Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.