From bulk, single-cell to spatial RNA sequencing

Pharmacologically targeting KRAS G12D in PDAC models: tumor cell intrinsic and extrinsic impact

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which new therapeutic interventions are needed. Here we assessed the cellular response to pharmacological KRAS inhibition, which target the central oncogenic factor in PDAC. In a panel of PDAC cell lines, pharmaceutical inhibition of KRAS ^G12D allele, with MRTX1133 yields variable efficacy in the suppression of cell growth and downstream gene expression programs in 2D culture. CRISPR screens identify new drivers for enhanced therapeutic response that regulate focal adhesion and signaling cascades, which were confirmed by gene specific knockdowns and combinatorial drug synergy. Interestingly, MRTX1133 is considerably more efficacious in the context of 3D cell cultures and in vivo PDAC patient-derived xenografts. In syngeneic models, KRAS ^G12D inhibition elicits potent tumor regression that did not occur in immune-deficient hosts. Digital spatial profiling on tumor tissues indicates that MRTX1133 activates interferon-γ signaling and induces antigen presentation that modulate the tumor microenvironment. Further investigation on the immunological response using single cell sequencing and multispectral imaging reveals that tumor regression is associated with suppression of neutrophils and influx of effector CD8 ⁺ T-cells. Thus, both tumor cell intrinsic and extrinsic events contribute to response and credential KRAS ^G12D inhibition as promising strategy for a large percentage of PDAC tumors.

Insights into high-grade serous carcinoma pathobiology using three-dimensional culture model systems

Epithelial ovarian cancer (EOC) research has become more complex as researchers try to fully understand the metastatic process. Especially as we delve into the concept of tumour dormancy, where cells transition between proliferative and dormant states to survive during disease progression. Thus, the in vitro models used to conduct this research need to reflect this vast biological complexity. The innovation behind the many three-dimensional (3D) spheroid models has been refined to easily generate reproducible spheroids so that we may understand the various molecular signaling changes of cells during metastasis and determine therapeutic efficacy of treatments. This ingenuity was then used to develop the 3D ex vivo patient-derived organoid model, as well as multiple co-culture model systems for EOC research. Although, researchers need to continue to push the boundaries of these current models for in vitro and even in vivo work in the future. In this review, we describe the 3D models already in use, where these models can be developed further and how we can use these models to gain the most knowledge on EOC pathogenesis and discover new targeted therapies.

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.

Single-cell chemokine receptor profiles delineate the immune contexture of tertiary lymphoid structures in head and neck squamous cell carcinoma

Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells associated with favourable prognosis and response to immunotherapy in cancer, but the immune architecture of TLSs remains poorly elucidated. Here, we hypothesize that the spatial architecture of leukocytes in TLSs can be reconstructed de novo, at least partially, by cell-inherent chemokine receptors profiles. Single-cell RNA-sequencing (scRNA-seq) revealed 47 subpopulations of leukocytes in head and neck squamous cell carcinoma (HNSC). Combined with bulk RNA-seq, we observed that CXCR3, CCR7, CCR6, CXCR5, and CCR1 are TLS-associated chemokine receptors. According to the spatial reference, the cellular atlas with TLS-associated chemokine receptors in HNSC TLSs was elaborately portrayed by multiplex immunohistochemistry (mIHC). Subsequently, we explored the functions and evolutionary trajectory of cells distributed in TLSs. Our investigation presents an approach to reconstructing the immune architecture of TLSs, which would help boost the antitumor immune response by inducing neogenesis TLSs in HNSC.

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation method pretrained with single-cell transcriptomics data. The scTransSort incorporates a method of representing genes as gene expression embedding blocks to reduce the sparsity of data used for cell type identification and reduce the computational complexity. The feature of scTransSort is that its implementation of intelligent information extraction for unordered data, automatically extracting valid features of cell types without the need for manually labeled features and additional references. In experiments on cells from 35 human and 26 mouse tissues, scTransSort successfully elucidated its high accuracy and high performance for cell type identification, and demonstrated its own high robustness and generalization ability.

Targeting the metastatic niche: Single-cell lineage tracing in prime time

Identification of actionable drug targets remains a rate-limiting step of, and one of the most prominent barriers to successful drug development for metastatic cancers. CRISPR-Cas9, a tool for making targeted genomic edits, has given rise to various novel applications that have greatly accelerated discovery in developmental biology. Recent work has coupled a CRISPR-Cas9-based lineage tracing platform with single-cell transcriptomics in the unexplored context of cancer metastasis. In this perspective, we briefly reflect on the development of these distinct technological advances and the process by which they have become integrated. We also highlight the importance of single-cell lineage tracing in oncology drug development and suggest the profound capacity of a high-resolution, computational approach to reshape cancer drug discovery by enabling identification of novel metastasis-specific drug targets and mechanisms of resistance.

Cell-specific vulnerability to metabolic failure: the crucial role of parvalbumin expressing neurons in creatine transporter deficiency

Mutations in the solute carrier family 6-member 8 (Slc6a8) gene, encoding the protein responsible for cellular creatine (Cr) uptake, cause Creatine Transporter Deficiency (CTD), an X-linked neurometabolic disorder presenting with intellectual disability, autistic-like features, and epilepsy. The pathological determinants of CTD are still poorly understood, hindering the development of therapies. In this study, we generated an extensive transcriptomic profile of CTD showing that Cr deficiency causes perturbations of gene expression in excitatory neurons, inhibitory cells, and oligodendrocytes which result in remodeling of circuit excitability and synaptic wiring. We also identified specific alterations of parvalbumin-expressing (PV⁺) interneurons, exhibiting a reduction in cellular and synaptic density, and a hypofunctional electrophysiological phenotype. Mice lacking Slc6a8 only in PV⁺ interneurons recapitulated numerous CTD features, including cognitive deterioration, impaired cortical processing and hyperexcitability of brain circuits, demonstrating that Cr deficit in PV⁺ interneurons is sufficient to determine the neurological phenotype of CTD. Moreover, a pharmacological treatment targeted to restore the efficiency of PV⁺ synapses significantly improved cortical activity in Slc6a8 knock-out animals. Altogether, these data demonstrate that Slc6a8 is critical for the normal function of PV⁺ interneurons and that impairment of these cells is central in the disease pathogenesis, suggesting a novel therapeutic venue for CTD.

Broadening horizons in mechanisms, management, and treatment of diabetic kidney disease

Diabetic kidney disease (DKD) is the first cause of end-stage kidney disease in patients with diabetes and its prevalence is increasing worldwide. It encompasses histological alterations that mainly affect the glomerular filtration unit, which include thickening of the basement membrane, mesangial cell proliferation, endothelial alteration, and podocyte injury. These morphological abnormalities further result in a persistent increase of urinary albumin-to-creatinine ratio and in a reduction of the estimated glomerular filtration rate. Several molecular and cellular mechanisms have been recognized, up to date, as major players in mediating such clinical and histological features and many more are being under investigation. This review summarizes the most recent advances in understanding cell death mechanisms, intracellular signaling pathways and molecular effectors that play a role in the onset and progression of diabetic kidney damage. Some of those molecular and cellular mechanisms have been already successfully targeted in preclinical models of DKD and, in some cases, strategies have been tested in clinical trials. Finally, this report sheds light on the relevance of novel pathways that may become therapeutic targets for future applications in DKD.

Improving head and neck cancer therapies by immunomodulation of the tumour microenvironment

Targeted immunotherapy has improved patient survival in head and neck squamous cell carcinoma (HNSCC), but less than 20% of patients produce a durable response to these treatments. Thus, new immunotherapies that consider all key players of the complex HNSCC tumour microenvironment (TME) are necessary to further enhance tumour-specific T cell responses in patients. HNSCC is an ideal tumour type in which to evaluate immune and non-immune cell differences because of two distinct TME aetiologies (human papillomavirus (HPV)-positive and HPV-negative disease), multiple anatomic sites for tumour growth, and clear distinctions between patients with locally advanced disease and those with recurrent and/or metastatic disease. Recent technological and scientific advancements have provided a more complete picture of all cellular constituents within this complex TME and have evaluated the interplay of both immune and non-immune cells within HNSCC. Here, we include a comprehensive analysis of the complete ecosystem of the HNSCC TME, performed utilizing data-rich resources such as The Cancer Genome Atlas, and cutting-edge techniques, such as single-cell RNA sequencing, high-dimensional flow cytometry and spatial multispectral imaging, to generate improved treatment strategies for this diverse disease.

Immunomodulatory Biomaterials and Emerging Analytical Techniques for Probing the Immune Micro-Environment

After implantation of a biomaterial, both the host immune system and properties of the material determine the local immune response. Through triggering or modulating the local immune response, materials can be designed towards a desired direction of promoting tissue repair or regeneration. High-throughput sequencing technologies such as single-cell RNA sequencing (scRNA-seq) emerging as a powerful tool for dissecting the immune micro-environment around biomaterials, have not been fully utilized in the field of soft tissue regeneration. In this review, we first discussed the procedures of foreign body reaction in brief. Then, we summarized the influences that physical and chemical modulation of biomaterials have on cell behaviors in the micro-environment. Finally, we discussed the application of scRNA-seq in probing the scaffold immune micro-environment and provided some reference to designing immunomodulatory biomaterials. The foreign body response consists of a series of biological reactions. Immunomodulatory materials regulate immune cell activation and polarization, mediate divergent local immune micro-environments and possess different tissue engineering functions. The manipulation of physical and chemical properties of scaffolds can modulate local immune responses, resulting in different outcomes of fibrosis or tissue regeneration. With the advancement of technology, emerging techniques such as scRNA-seq provide an unprecedented understanding of immune cell heterogeneity and plasticity in a scaffold-induced immune micro-environment at high resolution. The in-depth understanding of the interaction between scaffolds and the host immune system helps to provide clues for the design of biomaterials to optimize regeneration and promote a pro-regenerative local immune micro-environment.

Endothelial ADAM10 controls cellular response to oxLDL and its deficiency exacerbates atherosclerosis with intraplaque hemorrhage and neovascularization in mice

Introduction

The transmembrane protease A Disintegrin And Metalloproteinase 10 (ADAM10) displays a "pattern regulatory function," by cleaving a range of membrane-bound proteins. In endothelium, it regulates barrier function, leukocyte recruitment and angiogenesis. Previously, we showed that ADAM10 is expressed in human atherosclerotic plaques and associated with neovascularization. In this study, we aimed to determine the causal relevance of endothelial ADAM10 in murine atherosclerosis development in vivo.

Methods and results

Endothelial Adam10 deficiency (Adam10 ^ecko ) in Western-type diet (WTD) fed mice rendered atherogenic by adeno-associated virus-mediated PCSK9 overexpression showed markedly increased atherosclerotic lesion formation. Additionally, Adam10 deficiency was associated with an increased necrotic core and concomitant reduction in plaque macrophage content. Strikingly, while intraplaque hemorrhage and neovascularization are rarely observed in aortic roots of atherosclerotic mice after 12 weeks of WTD feeding, a majority of plaques in both brachiocephalic artery and aortic root of Adam10^ecko mice contained these features, suggestive of major plaque destabilization. In vitro, ADAM10 knockdown in human coronary artery endothelial cells (HCAECs) blunted the shedding of lectin-like oxidized LDL (oxLDL) receptor-1 (LOX-1) and increased endothelial inflammatory responses to oxLDL as witnessed by upregulated ICAM-1, VCAM-1, CCL5, and CXCL1 expression (which was diminished when LOX-1 was silenced) as well as activation of pro-inflammatory signaling pathways. LOX-1 shedding appeared also reduced in vivo, as soluble LOX-1 levels in plasma of Adam10^ecko mice was significantly reduced compared to wildtypes.

Discussion

Collectively, these results demonstrate that endothelial ADAM10 is atheroprotective, most likely by limiting oxLDL-induced inflammation besides its known role in pathological neovascularization. Our findings create novel opportunities to develop therapeutics targeting atherosclerotic plaque progression and stability, but at the same time warrant caution when considering to use ADAM10 inhibitors for therapy in other diseases.

Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy

Retinal neovascularization is one of the leading causes of vision loss and a hallmark of proliferative diabetic retinopathy (PDR). The immune system is observed to be involved in the pathogenesis of diabetic retinopathy (DR). The specific immune cell type that contributes to retinal neovascularization can be identified via a bioinformatics analysis of RNA sequencing (RNA-seq) data, known as deconvolution analysis. Previous study has identified the infiltration of macrophages in the retina of rats with hypoxia-induced retinal neovascularization and patients with PDR through a deconvolution algorithm, known as CIBERSORTx. Here, we describe the protocols of using CIBERSORTx to perform the deconvolution analysis and downstream analysis of RNA-seq data.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Zinc finger and SCAN domain containing 1, ZSCAN1, is a novel stemness-related tumor suppressor and transcriptional repressor in breast cancer targeting TAZ

Introduction

Cancer stem cells (CSCs) targeted therapy holds the potential for improving cancer management; identification of stemness-related genes in CSCs is necessary for its development.

Methods

The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets were used for survival analysis. ZSCAN1 correlated genes was identified by Spearman correlation analysis. Breast cancer stem-like cells (BCSLCs) were isolated by sorting CD44+CD24- cells from suspension cultured breast cancer (BC) spheroids. The sphere-forming capacity and sphere- and tumor-initiating capacities were determined by sphere formation and limiting dilution assays. The relative gene expression was determined by qRT-PCR, western blot. Lentivirus system was used for gene manipulation. Nuclear run-on assay was employed to examine the levels of nascent mRNAs. DNA pull-down and Chromatin immunoprecipitation (ChIP) assays were used for determining the interaction between protein and target DNA fragments. Luciferase reporter assay was used for evaluating the activity of the promoter.

Results and discussion

ZSCAN1 is aberrantly suppressed in BC, and this suppression indicates a bad prognosis. Ectopic expression of ZSCAN1 inhibited the proliferation, clonogenicity, and tumorigenicity of BC cells. ZSCAN1-overexpressing BCSLCs exhibited weakened stemness properties. Normal human mammary epithelial (HMLE) cells with ZSCAN1 depletion exhibited enhanced stemness properties. Mechanistic studies showed that ZSCAN1 directly binds to -951 ~ -925bp region of WWTR1 (encodes TAZ) promoter, inhibits WWTR1 transcription, thereby inhibiting the stemness of BCSCs. Our work thus revealed ZSCAN1 as a novel stemness-related tumor suppressor and transcriptional repressor in BC.

Application of Single-Cell RNA Sequencing in Ovarian Development

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women's health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.

What has single-cell transcriptomics taught us about long non-coding RNAs in the ventricular-subventricular zone?

Long non-coding RNA (lncRNA) function is mediated by the process of transcription or through transcript-dependent associations with proteins or nucleic acids to control gene regulatory networks. Many lncRNAs are transcribed in the ventricular-subventricular zone (V-SVZ), a postnatal neural stem cell niche. lncRNAs in the V-SVZ are implicated in neurodevelopmental disorders, cancer, and brain disease, but their functions are poorly understood. V-SVZ neurogenesis capacity declines with age due to stem cell depletion and resistance to neural stem cell activation. Here we analyzed V-SVZ transcriptomics by pooling current single-cell RNA-seq data. They showed consistent lncRNA expression during stem cell activation, lineage progression, and aging. In conjunction with epigenetic and genetic data, we predicted V-SVZ lncRNAs that regulate stem cell activation and differentiation. Some of the lncRNAs validate known epigenetic mechanisms, but most remain uninvestigated. Our analysis points to several lncRNAs that likely participate in key aspects of V-SVZ stem cell activation and neurogenesis in health and disease.

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Background

Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNA-seq) is an effective method for studying the changing omics of cells in complex TMEs.

Methods

The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.

Results

Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.

Conclusions

Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor.shinyapps.io/GCNBC/).

LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes

The identification of cell types in complex tissues is an important step in research into cellular heterogeneity in disease. We present a linear fast semi-supervised clustering (LFSC) algorithm that utilizes reference samples generated from bulk RNA sequencing data to identify cell types from single-cell transcriptomes. An anchor graph is constructed to depict the relationship between reference samples and cells. By applying a connectivity constraint to the learned graph, LFSC enables the preservation of the underlying cluster structure. Moreover, the overall complexity of LFSC is linear to the size of the data, which greatly improves effectiveness and efficiency. By applying LFSC to real single-cell RNA sequencing datasets, we discovered that it has superior performance over existing baseline methods in clustering accuracy and robustness. An application using infiltrating T cells in liver cancer demonstrates that LFSC can successfully find new cell types, discover differently expressed genes, and explore new cancer-associated biomarkers.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

How is spatial transcriptomics influencing cancer research and diagnostics?

[Formula: see text]

Spatial transcriptomics has continued to rise in popularity since it was developed in 2016; but how is it being utilized in tumor biology and diagnostics research?

Spatial Transcriptomics-Based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue

The intratumoral accumulation of nanomedicine has been considered a passive process, referred to as the enhanced permeability and retention effect. Recent studies have suggested that the tumor uptake of nanomedicines follows an energy-dependent pathway rather than being a passive process. Herein, to explore the factor candidates that are associated with nanomedicine tumor uptake, a molecular marker identification platform is developed by integrating microscopic fluorescence images of a nanomedicine distribution with spatial transcriptomics information. When this approach is applied to PEGylated liposomes, molecular markers related to hypoxia, glycolysis, and apoptosis can be identified as being related to the intratumoral distribution of the nanomedicine. It is expected that the method can be applied to explain the distribution of a wide range of nanomedicines and that the data obtained from this analysis can enhance the precise utilization of nanomedicines.

Insights into skeletal stem cells

The tissue-resident skeletal stem cells (SSCs), which are self-renewal and multipotent, continuously provide cells (including chondrocytes, bone cells, marrow adipocytes, and stromal cells) for the development and homeostasis of the skeletal system. In recent decade, utilizing fluorescence-activated cell sorting, lineage tracing, and single-cell sequencing, studies have identified various types of SSCs, plotted the lineage commitment trajectory, and partially revealed their properties under physiological and pathological conditions. In this review, we retrospect to SSCs identification and functional studies. We discuss the principles and approaches to identify bona fide SSCs, highlighting pioneering findings that plot the lineage atlas of SSCs. The roles of SSCs and progenitors in long bone, craniofacial tissues, and periosteum are systematically discussed. We further focus on disputes and challenges in SSC research.

Development of Single-Cell Transcriptomics and Its Application in COVID-19

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.

CAMML with the Integration of Marker Proteins (ChIMP)

MOTIVATION

Cell typing is a critical task in the analysis of single-cell data, particularly when studying complex diseased tissues. Unfortunately, the sparsity and noise of single-cell data make accurate cell typing of individual cells difficult. To address these challenges, we previously developed the CAMML method for multi-label cell typing of single-cell RNA-sequencing (scRNA-seq) data. CAMML uses weighted gene sets to score each profiled cell for multiple potential cell types. While CAMML outperforms other scRNA-seq cell typing techniques, it only leverages transcriptomic data so cannot take advantage of newer multi-omic single-cell assays that jointly profile gene expression and protein abundance (e.g. joint scRNA-seq/CITE-seq).

RESULTS

We developed the CAMML with the Integration of Marker Proteins (ChIMP) method to support multi-label cell typing of individual cells jointly profiled via scRNA-seq and CITE-seq. ChIMP combines cell type scores computed on scRNA-seq data via the CAMML approach with discretized CITE-seq measurements for cell type marker proteins. The multi-omic cell type scores generated by ChIMP allow researchers to more precisely and conservatively cell type joint scRNA-seq/CITE-seq data.

AVAILABILITY AND IMPLEMENTATION

An implementation of this work is available on CRAN at https://cran.r-project.org/web/packages/CAMML/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis

Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1–100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.

Single-cell transcriptome study in forensic medicine: prospective applications

Next-generation sequencing and single-cell RNA sequencing (scRNA-seq) technologies have advanced rapidly in recent years. scRNA-seq reveals the unique gene expression of each cell type, providing directions for exploring cell heterogeneity, cell type-specific responses to injury/disease, and the mechanisms underlying these processes. The development of sequencing technology and improved sequencing throughput have brought about a revolution in single-cell transcriptome study, bringing great benefits to the fields of medicine and biomedical science. From our perspective, certain issues in forensic medicine may potentially be addressed using single-cell transcriptome studies; however, this powerful technique has not yet attracted sufficient attention in forensic medicine-associated research. Therefore, examining and reviewing the latest developments and applications of single-cell transcriptome studies, we present our views on the future directions of forensic research using this technology, aiming to expand the frontiers of forensic science.

High-throughput sequencing unravels the cell heterogeneity of cerebrospinal fluid in the bacterial meningitis of children

Bacterial meningitis (BM) is a common life-threatening infection in children that occurs in the central nervous system (CNS). The cytologic examination of cerebrospinal fluid (CSF) is a key parameter in the diagnosis of BM, but the heterogeneity of cells in the CSF has not been elucidated, which limits the current understanding of BM neuroinflammation. In this study, CSF samples were collected from a number of BM patients who were in different stages of disease progression. Single-cell RNA-sequencing (scRNA-seq), with additional bulk transcriptome sequencing, was conducted to decipher the characteristics of CSF cells in BM progression. A total of 18 immune cell clusters in CSF were identified, including two neutrophils, two monocytes, one macrophage, four myeloid dendritic cells, five T cells, one natural killer cell, one B cell, one plasmacytoid dendritic cell, and one plasma cell subtype. Their population profiles and dynamics in the initial onset, remission, and recovery stages during BM progression were also characterized, which showed decreased proportions of myeloid cells and increased proportions of lymphoid cells with disease progression. One novel neutrophil subtype, FFAR2+TNFAIP6+ neutrophils, and one novel monocyte subtype, THBS1+IL1B+ monocytes, were discovered, and their quantity changes positively correlated with the intensity of the inflammatory response in the CSF during BM. In addition, the CSF of BM patients with unsatisfactory therapeutic responses presented with different cell heterogeneity compared to the CSF of BM patients with satisfactory therapeutic responses, and their CSF featured altered intercellular communications and increased proportions of type II myeloid dendritic cells and plasmacytoid dendritic cells. Moreover, the bulk transcriptome profiles of autologous CSF cells and peripheral blood leukocytes of BM patients showed that the immune cells in these two physiological compartments exhibited distinct immune responses under different onset conditions. In particular, the CSF cells showed a high expression of macrophage characteristic genes and a low expression of platelet characteristic genes compared with peripheral blood leukocytes. Our study conducted an in-depth exploration of the characteristics of CSF cells in BM progression, which provided novel insights into immune cell engagement in acute CNS infection.

Comprehensive transcriptome profiling of urothelial cells following TNFα stimulation in an in vitro interstitial cystitis/bladder pain syndrome model

Urothelial cells of the urinary bladder play a critical role in the development and progression of interstitial cystitis/bladder pain syndrome (IC/BPS), a chronic and debilitating inflammatory disease. Given the lack of data on the exact phenotype and function of urothelial cells in an inflammatory setting (as in IC/BPS), we performed the first in-depth characterization of these cells using RNA sequencing, qPCR, ELISA, Western blot, and immunofluorescence. After TNFα stimulation, urothelial cells in the in vitro model of IC/BPS showed marked upregulation of several proinflammatory mediators, such as SAA, C3, IFNGR1, IL1α, IL1β, IL8, IL23A, IL32, CXCL1, CXCL5, CXCL10, CXCL11, TNFAIPR, TNFRSF1B, and BIRC3, involved in processes and pathways of innate immunity, including granulocyte migration and chemotaxis, inflammatory response, and complement activation, as well as TLR-, NOD-like receptor- and NFkB-signaling pathways, suggesting their active role in shaping the local immune response of the bladder. Our study demonstrates that the TNFα-stimulated urothelial cells recapitulate key observations found in the bladders of patients with IC/BPS, underpinning their utility as a suitable in vitro model for understanding IC/BPS mechanisms and confirming the role of TNFα signaling as an important component of the associated pathology. The present study also identifies novel upregulated gene targets of TNFα in urothelial cells, including genes encoding the acute phase protein SAA, complement component C3, and the cytokine receptor IFNGR1, which could be exploited as therapeutic targets of IC/BPS. Altogether, our study provides a reference database of the phenotype of urothelial cells in an inflammatory environment that will not only increase our knowledge of their role in IC/BPS, but also advance our understanding of how urothelial cells shape tissue immunity in the bladder.

Tight gene co-expression in BCB positive cattle oocytes and their surrounding cumulus cells

BACKGROUND

Cytoplasmic and nuclear maturation of oocytes, as well as interaction with the surrounding cumulus cells, are important features relevant to the acquisition of developmental competence.

METHODS

Here, we utilized Brilliant cresyl blue (BCB) to distinguish cattle oocytes with low activity of the enzyme Glucose-6-Phosphate Dehydrogenase, and thus separated fully grown (BCB positive) oocytes from those in the growing phase (BCB negative). We then analyzed the developmental potential of these oocytes, mitochondrial DNA (mtDNA) copy number in single oocytes, and investigated the transcriptome of single oocytes and their surrounding cumulus cells of BCB positive versus BCB negative oocytes.

RESULTS

The BCB positive oocytes were twice as likely to produce a blastocyst in vitro compared to BCB- oocytes (P < 0.01). We determined that BCB negative oocytes have 1.3-fold more mtDNA copies than BCB positive oocytes (P = 0.004). There was no differential transcript abundance of genes expressed in oocytes, however, 172 genes were identified in cumulus cells with differential transcript abundance (FDR < 0.05) based on the BCB staining of their oocyte. Co-expression analysis between oocytes and their surrounding cumulus cells revealed a subset of genes whose co-expression in BCB positive oocytes (n = 75) and their surrounding cumulus cells (n = 108) compose a unique profile of the cumulus-oocyte complex.

CONCLUSIONS

If oocytes transition from BCB negative to BCB positive, there is a greater likelihood of producing a blastocyst, and a reduction of mtDNA copies, but there is no systematic variation of transcript abundance. Cumulus cells present changes in transcript abundance, which reflects in a dynamic co-expression between the oocyte and cumulus cells.

A Detailed Overview About the Single-Cell Analyses of Solid Tumors Focusing on Colorectal Cancer

In recent years, the evolution of the molecular biological technical background led to the widespread application of single-cell sequencing, a versatile tool particularly useful in the investigation of tumor heterogeneity. Even 10 years ago the comprehensive characterization of colorectal cancers by The Cancer Genome Atlas was based on measurements of bulk samples. Nowadays, with single-cell approaches, tumor heterogeneity, the tumor microenvironment, and the interplay between tumor cells and their surroundings can be described in unprecedented detail. In this review article we aimed to emphasize the importance of single-cell analyses by presenting tumor heterogeneity and the limitations of conventional investigational approaches, followed by an overview of the whole single-cell analytic workflow from sample isolation to amplification, sequencing and bioinformatic analysis and a review of recent literature regarding the single-cell analysis of colorectal cancers.

The Landscape of Noncoding RNA in Pulmonary Hypertension

The transcriptome of pulmonary hypertension (PH) is complex and highly genetically heterogeneous, with noncoding RNA transcripts playing crucial roles. The majority of RNAs in the noncoding transcriptome are long noncoding RNAs (lncRNAs) with less circular RNAs (circRNAs), which are two characteristics gaining increasing attention in the forefront of RNA research field. These noncoding transcripts (especially lncRNAs and circRNAs) exert important regulatory functions in PH and emerge as potential disease biomarkers and therapeutic targets. Recent technological advancements have established great momentum for discovery and functional characterization of ncRNAs, which include broad transcriptome sequencing such as bulk RNA-sequence, single-cell and spatial transcriptomics, and RNA-protein/RNA interactions. In this review, we summarize the current research on the classification, biogenesis, and the biological functions and molecular mechanisms of these noncoding RNAs (ncRNAs) involved in the pulmonary vascular remodeling in PH. Furthermore, we highlight the utility and challenges of using these ncRNAs as biomarkers and therapeutics in PH.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Integrated analysis of an in vivo model of intra-nasal exposure to instilled air pollutants reveals cell-type specific responses in the placenta

The placenta is a heterogeneous organ whose development involves complex interactions of trophoblasts with decidual, vascular, and immune cells at the fetal-maternal interface. It maintains a critical balance between maternal and fetal homeostasis. Placental dysfunction can lead to adverse pregnancy outcomes including intra-uterine growth restriction, pre-eclampsia, or pre-term birth. Exposure to environmental pollutants contributes to the development of placental abnormalities, with poorly understood molecular underpinning. Here we used a mouse (C57BL/6) model of environmental pollutant exposure by administration of a particulate matter (SRM1649b at 300 μg/day/mouse) suspension intra-nasally beginning 2 months before conception and during gestation, in comparison to saline-exposed controls. Placental transcriptomes, at day 19 of gestation, were determined using bulk RNA-seq from whole placentas of exposed (n = 4) and control (n = 4) animals and scRNAseq of three distinct placental layers, followed by flow cytometry analysis of the placental immune cell landscape. Our results indicate a reduction in vascular placental cells, especially cells responsible for structural integrity, and increase in trophoblast proliferation in animals exposed to particulate matter. Pollution-induced inflammation was also evident, especially in the decidual layer. These data indicate that environmental exposure to air pollutants triggers changes in the placental cellular composition, mediating adverse pregnancy outcomes.

Where Are They Now: Spatial and Molecular Diversity of Tissue-Resident Macrophages in the Kidney

Kidney resident macrophages (KRMs) are involved in homeostasis, phagocytosis, defense against infectious agents, response to insults, inflammation, and tissue repair. They also play critical roles in the pathogenesis and recovery from many kidney diseases such as acute kidney injury. KRMs historically have been studied as one homogenous population, but the wide-ranging roles and phenotypes observed suggest that there is greater heterogeneity than previously understood. Advancements in RNA sequencing technologies (single-cell RNA sequencing and spatial transcriptomics) have identified specific subsets of KRMs that are molecularly, functionally, and spatially distinct with dynamic changes after kidney injury. Multiple studies have identified unique markers that represent these subpopulations, permitting further characterization of the function and roles they play in the kidney. Understanding the diversity of KRM subpopulations will be key in the development of novel therapies used in treating kidney diseases and promoting kidney health.

Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.