Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs

By profiling gene expression in individual cells, single-cell RNA-sequencing (scRNA-seq) can resolve cellular heterogeneity and cell-type gene expression dynamics. Its application to time-series samples can identify temporal gene programs active in different cell types, for example, immune cells' responses to viral infection. However, current scRNA-seq analysis has limitations. One is the low number of genes detected per cell. The second is insufficient replicates (often 1-2) due to high experimental cost. The third lies in the data analysis-treating individual cells as independent measurements leads to inflated statistics. To address these, we explore a new computational framework, specifically whether "metacells" constructed to maintain cellular heterogeneity within individual cell types (or clusters) can be used as "replicates" for increasing statistical rigor. Toward this, we applied SEACells to a time-series scRNA-seq dataset from peripheral blood mononuclear cells (PBMCs) after SARS-CoV-2 infection to construct metacells, and used them in maSigPro for quadratic regression to find significantly differentially expressed genes (DEGs) over time, followed by clustering expression velocity trends. We showed that such metacells retained greater expression variances and produced more biologically meaningful DEGs compared to either metacells generated randomly or from simple pseudobulk methods. More specifically, this approach correctly identified the known ISG15 interferon response program in almost all PBMC cell types and many DEGs enriched in the previously defined SARS-CoV-2 infection response pathway. It also uncovered additional and more cell type-specific temporal gene expression programs. Overall, our results demonstrate that the metacell-pseudoreplicate strategy could potentially overcome the limitation of 1-2 replicates.

Stroma-associated FSTL3 is a factor of calcium channel-derived tumor fibrosis

Hepatocellular carcinoma (HCC) is the most widespread histological form of primary liver cancer, and it faces great diagnostic and therapeutic difficulties owing to its tumor diversity. Herein, we aim to establish a unique prognostic molecular subtype (MST) and based on this to find potential therapeutic targets to develop new immunotherapeutic strategies. Using calcium channel molecules expression-based consensus clustering, we screened 371 HCC patients from The Cancer Genome Atlas to screen for possible MSTs. We distinguished core differential gene modules between varying MSTs, and Tumor Immune Dysfunction and Exclusion scores were employed for the reliable assessment of HCC patient immunotherapeutic response rate. Immunohistochemistry and Immunofluorescence staining were used for validation of predicted immunotherapy outcomes and underlying biological mechanisms, respectively. We identified two MSTs with different clinical characteristics and prognoses. Based on the significant differences between the two MSTs, we further identified Follistatin-like 3 (FSTL3) as a potential indicator of immunotherapy resistance and validated this result in our own cohort. Finally, we found that FSTL3 is predominantly expressed in HCC stromal components and that it is a factor in enhancing fibroblast-M2 macrophage signaling crosstalk, the function of which is relevant to the pathogenesis of HCC. The presence of two MSTs associated with the calcium channel phenotype in HCC patients may provide promising directions for overcoming immunotherapy resistance in HCC, and the promotion of FSTL3 expressed in stromal components for HCC hyperfibrosis may be responsible for the poor response rate to immunotherapy in Cluster 2 (C2) patients.

ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?

ChatGPT, GPT-4, and Bard are highly advanced natural language process-based computer programs (chatbots) that simulate and process human conversation in written or spoken form. Recently released by the company OpenAI, ChatGPT was trained on billions of unknown text elements (tokens) and rapidly gained wide attention for its ability to respond to questions in an articulate manner across a wide range of knowledge domains. These potentially disruptive large language model (LLM) technologies have a broad range of conceivable applications in medicine and medical microbiology. In this opinion article, I describe how chatbot technologies work and discuss the strengths and weaknesses of ChatGPT, GPT-4, and other LLMs for applications in the routine diagnostic laboratory, focusing on various use cases for the pre- to post-analytical process.

An Analysis Regarding the Association Between the Nuclear Pore Complex (NPC) and Hepatocellular Carcinoma (HCC)

Background

The nuclear pore complex (NPC) is the main mediator of nuclear and cytoplasmic communication, and delaying or blocking nuclear RNA export and protein shuttling can inhibit cell proliferation and induce apoptosis. Although NPC is a research hotspot in structural biology, relevant studies in hepatocellular carcinoma are scarce, especially in terms of translation into clinical practice.

Methods

This study used a bioinformatics approach combining validation experiments to investigate the biological mechanisms that may be related with NPC. A series of experiments performed to explore the function of the Targeting protein for Xenopus kinesin-like protein 2 (TPX2) in HCC.

Results

Patients with HCC can be divided into two NPC clusters. Patients with high NPC levels (C1) had a shorter survival time than those with low NPC levels (C2) and are characterised by high levels of proliferative signals. We demonstrated that TPX2 regulates HCC growth and inhibits apoptosis in an NPC-dependent manner and contributes to the maintenance of HCC stemness. We developed the NPCScore to predict the prognosis and degree of differentiation in HCC patients.

Conclusion

NPC plays an important role in the malignant proliferation of HCC. Assessing NPC expression patterns could help enhance our understanding of tumor cell proliferation and could guide more effective chemotherapeutic strategies.

Loss of ndrg2 Function Is Involved in Notch Activation in Neuromast Hair Cell Regeneration in Zebrafish

The regeneration of hair cells in zebrafish is a complex process involving the precise regulation of multiple signaling pathways, but this complicated regulatory network is not fully understood. Current research has primarily focused on finding molecules and pathways that can regulate hair cell regeneration and restore hair cell functions. Here, we show the role of N-Myc downstream regulated gene 2 (ndrg2) in zebrafish hair cell regeneration. We first found that ndrg2 was dynamically expressed in neuromasts of the developing zebrafish, and this expression was increased after neomycin-induced hair cell damage. Then, ndrg2 loss-of-function larvae showed reduced numbers of regenerated hair cells but increased numbers of supporting cells after neomycin exposure. By in situ hybridization, we further observed that ndrg2 loss of function resulted in the activation of Notch signaling and downregulation of atoh1a during hair cell regeneration in vivo. Additionally, blocking Notch signaling rescued the number of regenerated hair cells in ndrg2-deficient larvae. Together, this study provides evidence for the role of ndrg2 in regulating hair cell regeneration in zebrafish neuromasts.

Density-based detection of cell transition states to construct disparate and bifurcating trajectories

Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Single-cell RNA-sequencing of zebrafish hair cells reveals novel genes potentially involved in hearing loss

Hair cells play key roles in hearing and balance, and hair cell loss would result in hearing loss or vestibular dysfunction. Cellular and molecular research in hair cell biology provides us a better understanding of hearing and deafness. Zebrafish, owing to their hair cell-enriched organs, have been widely applied in hair cell-related research worldwide. Similar to mammals, zebrafish have inner ear hair cells. In addition, they also have lateral line neuromast hair cells. These different types of hair cells vary in morphology and function. However, systematic analysis of their molecular characteristics remains lacking. In this study, we analyzed the GFP+ cells isolated from Tg(Brn3c:mGFP) larvae with GFP expression in all hair cells using single-cell RNA-sequencing (scRNA-seq). Three subtypes of hair cells, namely macula hair cell (MHC), crista hair cell (CHC), and neuromast hair cell (NHC), were characterized and validated by whole-mount in situ hybridization analysis of marker genes. The hair cell scRNA-seq data revealed hair cell-specific genes, including hearing loss genes that have been identified in humans and novel genes potentially involved in hair cell formation and function. Two novel genes were discovered to specifically function in NHCs and MHCs, corresponding to their specific expression in NHCs and MHCs. This study allows us to understand the specific genes in hair cell subpopulations of zebrafish, which will shed light on the genetics of both human vestibular and cochlear hair cell function.

New insights into Human Hematopoietic Stem and Progenitor Cells via Single-Cell Omics

Residing at the apex of the hematopoietic hierarchy, hematopoietic stem and progenitor cells (HSPCs) give rise to all mature blood cells. In the last decade, significant progress has been made in single-cell RNA sequencing as well as multi-omics technologies that have facilitated elucidation of the heterogeneity of previously defined human HSPCs. From the embryonic stage through the adult stage to aging, single-cell studies have enabled us to trace the origins of hematopoietic stem cells (HSCs), demonstrating different hematopoietic differentiation during development, as well as identifying novel cell populations. In both hematological benign diseases and malignancies, single-cell omics technologies have begun to reveal tissue heterogeneity and have permitted mapping of microenvironmental ecosystems and tracking of cell subclones, thereby greatly broadening our understanding of disease development. Furthermore, advances have also been made in elucidating the molecular mechanisms for relapse and identifying therapeutic targets of hematological disorders and other non-hematological diseases. Extensive exploration of hematopoiesis at the single-cell level may thus have great potential for broad clinical applications of HSPCs, as well as disease prognosis.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Multiomics integration-based molecular characterizations of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation. This review summarizes various aspects of understanding multiomics integration-based molecular characterizations of COVID-19, which to date include the integration of transcriptomics, proteomics, genomics, lipidomics, immunomics and metabolomics to explore virus targets and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers an abridgment of omics investigations related to disease pathogenesis and virulence, the role of host genetic variation and a broad array of immune and inflammatory phenotypes contributing to understanding COVID-19 traits. Insights into this review, which combines existing strategies and multiomics integration profiling, may help further advance our knowledge of COVID-19.