Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Identification of potential characteristic genes in chronic skin infections through RNA sequencing and immunohistochemical analysis

The objective of the present study was to perform RNA sequencing and immunohistochemical analysis on skin specimens obtained from healthy individuals and individuals afflicted with prolonged skin infections. Bioinformatics methodologies were used to scrutinize the RNA sequencing data with the intention of pinpointing distinctive gene signatures associated with chronic skin infections. Skin tissue samples were collected from 11 individuals (4 subjects healthy and 7 patients with chronic skin infections) at the Affiliated Hospital of Southwest Medical University (Luzhou, China). The iDEP tool identified differentially expressed genes (DEGs) with log2 (fold change) ≥2 and q-value ≤0.01. Functional enrichment analysis using Gene Ontology and KEGG databases via the oebiotech online tool was then performed to determine the biological functions and pathways related to these DEGs. A protein-protein interaction network of DEGs identified HIF1A as a potential key gene. Subsequent immunohistochemistry analyses were performed on the samples to assess any variations in HIF1A expression. A total of 900 DEGs, 365 upregulated and 535 downregulated, were observed between the normal and chronic infection groups. The identified DEGs were found to serve a role in various biological processes, including 'hypoxia adaptation', 'angiogenesis', 'cell adhesion' and 'regulation of positive cell migration'. Additionally, these genes were revealed to be involved in the 'TGF-β', 'PI3K-Akt' and 'IL-17' signaling pathways. HIF1A and nine other genes were identified as central nodes in the PPI network. HIF1A expression was higher in chronically infected skin samples than in healthy samples, indicating its potential as a novel research target.

Single-cell sequencing applications in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous group of malignancies with poor prognosis. AML result from the proliferation of immature myeloid cells blocked at a variable stage of differentiation. Beyond inter-patient heterogeneity, AMLs are characterized by genetic and phenotypic intra-patient heterogeneity. Despite major advances in deciphering AML biology with bulk sequencing studies, pivotal questions remain unanswered. Analyses at the single-cell level could thus transform our understanding of these neoplasms. We review recent progresses in single-cell sequencing technologies from cell processing to bioinformatic pipelines. We next discuss how single-cell applications have helped understand the genetic and functional intra-leukemic heterogeneity, emphasizing aspects related to leukemic stem cells, clonal evolution and measurable residual disease (MRD) monitoring. We finally delineate how single-cell technologies could be implemented in routine clinical practice to improve patient management.

Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice

Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.

Single-cell atlases reveal leaf cell-type-specific regulation of metal transporters in the hyperaccumulator Sedum alfredii under cadmium stress

Hyperaccumulation in plants is a complex and dynamic biological process. Sedum alfredii, the most studied Cd hyperaccumulator, can accumulate up to 9000 mg kg-1 Cd in its leaves without suffering toxicity. Although several studies have reported the molecular mechanisms of Cd hyperaccumulation, our understanding of the cell-type-specific transcriptional regulation induced by Cd remains limited. In this study, the first full-length transcriptome of S. alfredii was generated using the PacBio Iso-Seq technology. A total of 18,718,513 subreads (39.90 Gb) were obtained, with an average length of 2133 bp. The single-cell RNA sequencing was employed on leaves of S. alfredii grown under Cd stress. A total of 12,616 high-quality single cells were derived from the control and Cd-treatment samples of S. alfredii leaves. Based on cell heterogeneity and the expression profiles of previously reported marker genes, seven cell types with 12 transcriptionally distinct cell clusters were identified, thereby constructing the first single-cell atlas for S. alfredii leaves. Metal transporters such as CAX5, COPT5, ZIP5, YSL7, and MTP1 were up-regulated in different cell types of S. alfredii leaves under Cd stress. The distinctive gene expression patterns of metal transporters indicate special gene regulatory networks underlying Cd tolerance and hyperaccumulation in S. alfredii. Collectively, our findings are the first observation of the cellular and molecular responses of S. alfredii leaves under Cd stress and lay the cornerstone for future hyperaccumulator scRNA-seq investigations.

FixNCut: A Practical Guide to Sample Preservation by Reversible Fixation for Single Cell Assays

The quality of standard single-cell experiments often depends on the immediate processing of cells or tissues post-harvest to preserve fragile and vulnerable cell populations, unless the samples are adequately fixed and stored. Despite the recent rise in popularity of probe-based and aldehyde-fixed RNA assays, these methods face limitations in species and target availability and are not suitable for immunoprofiling or assessing chromatin accessibility. Recently, a reversible fixation strategy known as FixNCut has been successfully deployed to separate sampling from downstream applications in a reproducible and robust manner, avoiding stress or necrosis-related artifacts. In this article, we present an optimized and robust practical guide to the FixNCut protocol to aid the end-to-end adaptation of this versatile method. This protocol not only decouples tissue or cell harvesting from single-cell assays but also enables a flexible and decentralized workflow that unlocks the potential for single-cell analysis as well as unconventional study designs that were previously considered unfeasible. Key features • Reversible fixation: Preserves cellular and molecular structures with the option to later reverse the fixation for downstream applications, maintaining cell integrity • Compatibility with single-cell assays: Supports single-cell genomic assays such as scRNA-seq and ATAC-seq, essential for high-resolution analysis of cell function and gene expression • Flexibility in sample handling: Allows immediate fixation post-collection, decoupling sample processing from analysis, beneficial in settings where immediate processing is impractical • Preservation of RNA and DNA integrity: Effectively preserves RNA and DNA, reducing degradation to ensure accurate transcriptomic and genomic profiling • Suitability for various biological samples: Applicable to a wide range of biological samples, including tissues and cell suspensions, whether freshly isolated or post-dissociated • Enables multi-center studies: Facilitates collaborative research across multiple centers by allowing sample fixation at the point of collection, enhancing research scale and diversity • Avoidance of artifacts: Minimizes stress or necrosis-related artifacts, preserving the natural cellular physiology for accurate genomic and transcriptomic analysis.

Dissecting viral infections, one cell at a time, by single-cell technologies

The meteoric rise of single-cell genomic technologies, especially of single-cell RNA-sequencing (scRNA-seq), has revolutionized several fields of cellular biology, especially immunology, oncology, neuroscience and developmental biology. While the field of virology has been relatively slow to adopt these technological advances, many works have shed new light on the fascinating interactions of viruses with their hosts using single cell technologies. One clear example is the multitude of studies dissecting viral infections by single-cell sequencing technologies during the recent COVID-19 pandemic. In this review we will detail the advantages of studying viral infections at a single-cell level, how scRNA-seq technologies can be used to achieve this goal and the associated technical limitations, challenges and solutions. We will highlight recent biological discoveries and breakthroughs in virology enabled by single-cell analyses and will end by discussing possible future directions of the field. Given the rate of publications in this exciting new frontier of virology, we have likely missed some important works and we apologize in advance to the researchers whose work we have failed to cite.

Current computational methods for spatial transcriptomics in cancer biology

Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Regulation of blood-brain barrier integrity by Dmp1-expressing astrocytes through mitochondrial transfer

The blood-brain barrier (BBB) acts as the crucial physical filtration structure in the central nervous system. Here, we investigate the role of a specific subset of astrocytes in the regulation of BBB integrity. We showed that Dmp1-expressing astrocytes transfer mitochondria to endothelial cells via their endfeet for maintaining BBB integrity. Deletion of the Mitofusin 2 (Mfn2) gene in Dmp1-expressing astrocytes inhibited the mitochondrial transfer and caused BBB leakage. In addition, the decrease of MFN2 in astrocytes contributes to the age-associated reduction of mitochondrial transfer efficiency and thus compromises the integrity of BBB. Together, we describe a mechanism in which astrocytes regulate BBB integrity through mitochondrial transfer. Our findings provide innnovative insights into the cellular framework that underpins the progressive breakdown of BBB associated with aging and disease.

Therapeutic targets of formononetin for treating prostate cancer at the single-cell level

Prostate cancer is one of the serious health problems of older male, about 13% of male was affected by prostate cancer. Prostate cancer is highly heterogeneity disease with complex molecular and genetic alterations. So, targeting the gene candidates in prostate cancer in single-cell level can be a promising approach for treating prostate cancer. In the present study, we analyzed the single cell sequencing data obtained from 2 previous reports to determine the differential gene expression of prostate cancer in single-cell level. By using the network pharmacology analysis, we identified the therapeutic targets of formononetin in immune cells and tissue cells of prostate cancer. We then applied molecular docking to determine the possible direct binding of formononetin to its target proteins. Our result identified a cluster of differential gene expression in prostate cancer which can serve as novel biomarkers such as immunoglobulin kappa C for prostate cancer prognosis. The result of network pharmacology delineated the roles of formononetin's targets such CD74 and THBS1 in immune cells' function of prostate cancer. Also, formononetin targeted insulin receptor and zinc-alpha-2-glycoprotein which play important roles in metabolisms of tissue cells of prostate cancer. The result of molecular docking suggested the direct binding of formononetin to its target proteins including INSR, TNF, and CXCR4. Finally, we validated our findings by using formononetin-treated human prostate cancer cell DU145. For the first time, our result suggested the use of formononetin for treating prostate cancer through targeting different cell types in a single-cell level.

Understanding glioblastoma at the single-cell level: Recent advances and future challenges

Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.

Single-cell and spatial RNA sequencing reveal the spatiotemporal trajectories of fruit senescence

The senescence of fruit is a complex physiological process, with various cell types within the pericarp, making it highly challenging to elucidate their individual roles in fruit senescence. In this study, a single-cell expression atlas of the pericarp of pitaya (Hylocereus undatus) is constructed, revealing exocarp and mesocarp cells undergoing the most significant changes during the fruit senescence process. Pseudotime analysis establishes cellular differentiation and gene expression trajectories during senescence. Early-stage oxidative stress imbalance is followed by the activation of resistance in exocarp cells, subsequently senescence-associated proteins accumulate in the mesocarp cells at late-stage senescence. The central role of the early response factor HuCMB1 is unveiled in the senescence regulatory network. This study provides a spatiotemporal perspective for a deeper understanding of the dynamic senescence process in plants.

Application of Single-Cell Sequencing Technology in Research on Colorectal Cancer

Colorectal cancer (CRC) is the third most prevalent and second most lethal cancer globally, with gene mutations and tumor metastasis contributing to its poor prognosis. Single-cell sequencing technology enables high-throughput analysis of the genome, transcriptome, and epigenetic landscapes at the single-cell level. It offers significant insights into analyzing the tumor immune microenvironment, detecting tumor heterogeneity, exploring metastasis mechanisms, and monitoring circulating tumor cells (CTCs). This article provides a brief overview of the technical procedure and data processing involved in single-cell sequencing. It also reviews the current applications of single-cell sequencing in CRC research, aiming to enhance the understanding of intratumoral heterogeneity, CRC development, CTCs, and novel drug targets. By exploring the diverse molecular and clinicopathological characteristics of tumor heterogeneity using single-cell sequencing, valuable insights can be gained into early diagnosis, therapy, and prognosis of CRC. Thus, this review serves as a valuable resource for identifying prognostic markers, discovering new therapeutic targets, and advancing personalized therapy in CRC.

A laboratory framework for ongoing optimization of amplification-based genomic surveillance programs

IMPORTANCE

This study provides a laboratory framework to ensure ongoing relevance and performance of amplification-based whole genome sequencing to strengthen public health surveillance during extended outbreaks or pandemics. The framework integrates regular reviews of the performance of a genomic surveillance system and highlights the importance of ongoing monitoring and the identification and implementation of improvements to whole genome sequencing methods to enhance public health responses to pathogen outbreaks.

Integrating scRNA-seq and bulk RNA-seq to characterize infiltrating cells in the colorectal cancer tumor microenvironment and construct molecular risk models

Colorectal cancer (CRC) is a malignancy that is both highly lethal and heterogeneous. Although the correlation between intra-tumoral genetic and functional heterogeneity and cancer clinical prognosis is well-established, the underlying mechanism in CRC remains inadequately understood. Utilizing scRNA-seq data from GEO database, we re-isolated distinct subsets of cells, constructed a CRC tumor-related cell differentiation trajectory, and conducted cell-cell communication analysis to investigate potential interactions across cell clusters. A prognostic model was built by integrating scRNA-seq results with TCGA bulk RNA-seq data through univariate, LASSO, and multivariate Cox regression analyses. Eleven distinct cell types were identified, with Epithelial cells, Fibroblasts, and Mast cells exhibiting significant differences between CRC and healthy controls. T cells were observed to engage in extensive interactions with other cell types. Utilizing the 741 signature genes, prognostic risk score model was constructed. Patients with high-risk scores exhibited a significant correlation with unfavorable survival outcomes, high-stage tumors, metastasis, and low responsiveness to chemotherapy. The model demonstrated a strong predictive performance across five validation cohorts. Our investigation involved an analysis of the cellular composition and interactions of infiltrates within the microenvironment, and we developed a prognostic model. This model provides valuable insights into the prognosis and therapeutic evaluation of CRC.

Deciphering tumor-infiltrating dendritic cells in the single-cell era

Dendritic cells (DCs) serve as a pivotal link connecting innate and adaptive immunity by processing tumor-derived antigens and activating T cells. The advent of single-cell sequencing has revolutionized the categorization of DCs, enabling a high-resolution characterization of the previously unrecognized diversity of DC populations infiltrating the intricate tumor microenvironment (TME). The application of single-cell sequencing technologies has effectively elucidated the heterogeneity of DCs present in the tumor milieu, yielding invaluable insights into their subpopulation structures and functional diversity. This review provides a comprehensive summary of the current state of knowledge regarding DC subtypes in the TME, drawing from single-cell studies conducted across various human tumors. We focused on the categorization, functions, and interactions of distinct DC subsets, emphasizing their crucial roles in orchestrating tumor-related immune responses. Additionally, we delve into the potential implications of these findings for the identification of predictive biomarkers and therapeutic targets. Enhanced insight into the intricate interplay between DCs and the TME promises to advance our comprehension of tumor immunity and, in turn, pave the way for the development of more efficacious cancer immunotherapies.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Cell-type-specific molecular characterization of cells from circulation and kidney in IgA nephropathy with nephrotic syndrome

Nephrotic syndrome (NS) is a relatively rare and serious presentation of IgA nephropathy (IgAN) (NS-IgAN). Previous research has suggested that the pathogenesis of NS-IgAN may involve circulating immune imbalance and kidney injury; however, this has yet to be fully elucidated. To investigate the cellular and molecular status of NS-IgAN, we performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) and kidney cells from pediatric patients diagnosed with NS-IgAN by renal biopsy. Consistently, the proportion of intermediate monocytes (IMs) in NS-IgAN patients was higher than in healthy controls. Furthermore, flow cytometry confirmed that IMs were significantly increased in pediatric patients with NS. The characteristic expression of VSIG4 and MHC class II molecules and an increase in oxidative phosphorylation may be important features of IMs in NS-IgAN. Notably, we found that the expression level of CCR2 was significantly increased in the CMs, IMs, and NCMs of patients with NS-IgAN. This may be related to kidney injury. Regulatory T cells (Tregs) are classified into two subsets of cells: Treg1 (CCR7 high, TCF7 high, and HLA-DR low) and Treg2 (CCR7 low, TCF7 low, and HLA-DR high). We found that the levels of Treg2 cells expressed significant levels of CCR4 and GATA3, which may be related to the recovery of kidney injury. The state of NS in patients was closely related to podocyte injury. The expression levels of CCL2, PRSS23, and genes related to epithelial-mesenchymal transition were significantly increased in podocytes from NS-IgAN patients. These represent key features of podocyte injury. Our analysis suggests that PTGDS is significantly downregulated following injury and may represent a new marker for podocytes. In this study, we systematically analyzed molecular events in the circulatory system and kidney tissue of pediatric patients with NS-IgAN, which provides new insights for targeted therapy in the future.

Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues

Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.

Deep learning applications in single-cell genomics and transcriptomics data analysis

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

Senescence in cancer: Advances in detection and treatment modalities

Senescence is a form of irreversible cell cycle arrest. Senescence plays a dual role in cancer, as both a tumor suppressor by preventing the growth of damaged cells and a cancer promoter by creating an inflammatory milieu. Stress-induced premature senescence (SIPS) and replicative senescence are the two major sub-types of senescence. Senescence plays a dual role in cancer, depending on the context and kind of senescence involved. SIPS can cause cancer by nurturing an inflammatory environment, whereas replicative senescence may prevent cancer. Major pathways that are involved in senescence are the p53-p21, p16INK4A-Rb pathway along with mTOR, MAPK, and PI3K pathways. The lack of universal senescence markers makes it difficult to identify senescent cells in vivo. A combination of reliable detection methods of senescent cells in vivo is of utmost importance and will help in early detection and open new avenues for future treatment. New strategies that are being developed in order to tackle these shortcomings are in the field of fluorescent probes, nanoparticles, positron emission tomography probes, biosensors, and the detection of cell-free DNA from liquid biopsies. Along with detection, eradication of these senescent cells is also important to prevent cancer reoccurrence. Recently, the field of nano-senolytic and immunotherapy has also been emerging. This review provides up-to-date information on the various types of advancements made in the field of detection and treatment modalities for senescent cells that hold promise for the future treatment and prognosis of cancer, as well as their limitations and potential solutions.

Sample Collection Methods in Upper Gastrointestinal Research

In recent years, significant translational research advances have been made in the upper gastrointestinal (GI) research field. Endoscopic evaluation is a reasonable option for acquiring upper GI tissue for research purposes because it has minimal risk and can be applied to unresectable gastric cancer. The optimal number of biopsy samples and sample storage is crucial and might influence results. Furthermore, the methods for sample acquisition can be applied differently according to the research purpose; however, there have been few reports on methods for sample collection from endoscopic biopsies. In this review, we suggested a protocol for collecting study samples for upper GI research, including microbiome, DNA, RNA, protein, single-cell RNA sequencing, and organoid culture, through a comprehensive literature review. For microbiome analysis, one or two pieces of biopsied material obtained using standard endoscopic forceps may be sufficient. Additionally, 5 mL of gastric fluid and 3-4 mL of saliva is recommended for microbiome analyses. At least one gastric biopsy tissue is necessary for most DNA or RNA analyses, while proteomics analysis may require at least 2-3 biopsy tissues. Single cell-RNA sequencing requires at least 3-5 tissues and additional 1-2 tissues, if possible. For successful organoid culture, multiple sampling is necessary to improve the quality of specimens.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Single cell transcriptome sequencing of stimulated and frozen human peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) are blood cells that are a critical part of the immune system used to fight off infection, defending our bodies from harmful pathogens. In biomedical research, PBMCs are commonly used to study global immune response to disease outbreak and progression, pathogen infections, for vaccine development and a multitude of other clinical applications. Over the past few years, the revolution in single-cell RNA sequencing (scRNA-seq) has enabled an unbiased quantification of gene expression in thousands of individual cells, which provides a more efficient tool to decipher the immune system in human diseases. In this work, we generate scRNA-seq data from human PBMCs at high sequencing depth (>100,000 reads/cell) for more than 30,000 cells, in resting, stimulated, fresh and frozen conditions. The data generated can be used for benchmarking batch correction and data integration methods, and to study the effect of freezing-thawing cycles on the quality of immune cell populations and their transcriptomic profiles.

RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics

Spatial transcriptomics (ST) profiles gene expression in intact tissues. However, ST data measured at each spatial location may represent gene expression of multiple cell types, making it difficult to identify cell-type-specific transcriptional variation across spatial contexts. Existing cell-type deconvolutions of ST data often require single-cell transcriptomic references, which can be limited by availability, completeness and platform effect of such references. We present RETROFIT, a reference-free Bayesian method that produces sparse and interpretable solutions to deconvolve cell types underlying each location independent of single-cell transcriptomic references. Results from synthetic and real ST datasets acquired by Slide-seq and Visium platforms demonstrate that RETROFIT outperforms existing reference-based and reference-free methods in estimating cell-type composition and reconstructing gene expression. Applying RETROFIT to human intestinal development ST data reveals spatiotemporal patterns of cellular composition and transcriptional specificity. RETROFIT is available at https://bioconductor.org/packages/release/bioc/html/retrofit.html.

Approaches for single-cell RNA sequencing across tissues and cell types

Single-cell sequencing of RNA (scRNA-seq) has advanced our understanding of cellular heterogeneity and signaling in developmental biology and disease. A large number of complementary assays have been developed to profile transcriptomes of individual cells, also in combination with other readouts, such as chromatin accessibility or antibody-based analysis of protein surface markers. As scRNA-seq technologies are advancing fast, it is challenging to establish robust workflows and up-to-date protocols that are best suited to address the large range of research questions. Here, we review scRNA-seq techniques from mRNA end-counting to total RNA in relation to their specific features and outline the necessary sample preparation steps and quality control measures. Based on our experience in dealing with the continuously growing portfolio from the perspective of a central single-cell facility, we aim to provide guidance on how workflows can be best automatized and share our experience in coping with the continuous expansion of scRNA-seq techniques.

High-throughput microfluidic droplets in biomolecular analytical system: A review

Droplet microfluidic technology has revolutionized biomolecular analytical research, as it has the capability to reserve the genotype-to-phenotype linkage and assist for revealing the heterogeneity. Massive and uniform picolitre droplets feature dividing solution to the level that single cell and single molecule in each droplet can be visualized, barcoded, and analyzed. Then, the droplet assays can unfold intensive genomic data, offer high sensitivity, and screen and sort from a large number of combinations or phenotypes. Based on these unique advantages, this review focuses on up-to-date research concerning diverse screening applications utilizing droplet microfluidic technology. The emerging progress of droplet microfluidic technology is first introduced, including efficient and scaling-up in droplets encapsulation, and prevalent batch operations. Then the new implementations of droplet-based digital detection assays and single-cell muti-omics sequencing are briefly examined, along with related applications such as drug susceptibility testing, multiplexing for cancer subtype identification, interactions of virus-to-host, and multimodal and spatiotemporal analysis. Meanwhile, we specialize in droplet-based large-scale combinational screening regarding desired phenotypes, with an emphasis on sorting for immune cells, antibodies, enzymatic properties, and proteins produced by directed evolution methods. Finally, some challenges, deployment and future perspective of droplet microfluidics technology in practice are also discussed.

Current Status and Prospects of the Single-Cell Sequencing Technologies for Revealing the Pathogenesis of Pregnancy-Associated Disorders

Single-cell RNA sequencing (scRNA-seq) is a method that focuses on the analysis of gene expression profile in individual cells. This method has been successfully applied to answer the challenging questions of the pathogenesis of multifactorial diseases and open up new possibilities in the prognosis and prevention of reproductive diseases. In this article, we have reviewed the application of scRNA-seq to the analysis of the various cell types and their gene expression changes in normal pregnancy and pregnancy complications. The main principle, advantages, and limitations of single-cell technologies and data analysis methods are described. We discuss the possibilities of using the scRNA-seq method for solving the fundamental and applied tasks related to various pregnancy-associated disorders. Finally, we provide an overview of the scRNA-seq findings for the common pregnancy-associated conditions, such as hyperglycemia in pregnancy, recurrent pregnancy loss, preterm labor, polycystic ovary syndrome, and pre-eclampsia.

Single-cell and spatial sequencing application in pathology

Traditionally, diagnostic pathology uses histology representing structural alterations in a disease's cells and tissues. In many cases, however, it is supplemented by other morphology-based methods such as immunohistochemistry and fluorescent in situ hybridization. Single-cell RNA sequencing (scRNA-seq) is one of the strategies that may help tackle the heterogeneous cells in a disease, but it does not usually provide histologic information. Spatial sequencing is designed to assign cell types, subtypes, or states according to the mRNA expression on a histological section by RNA sequencing. It can provide mRNA expressions not only of diseased cells, such as cancer cells but also of stromal cells, such as immune cells, fibroblasts, and vascular cells. In this review, we studied current methods of spatial transcriptome sequencing based on their technical backgrounds, tissue preparation, and analytic procedures. With the pathology examples, useful recommendations for pathologists who are just getting started to use spatial sequencing analysis in research are provided here. In addition, leveraging spatial sequencing by integration with scRNA-seq is reviewed. With the advantages of simultaneous histologic and single-cell information, spatial sequencing may give a molecular basis for pathological diagnosis, improve our understanding of diseases, and have potential clinical applications in prognostics and diagnostic pathology.

Practical Considerations for Complex Tissue Dissociation for Single-Cell Transcriptomics

Single-cell and single-nucleus RNA sequencing have revolutionized biomedical research, allowing analysis of complex tissues, identification of novel cell types, and mapping of development as well as disease states. Successful application of this technology critically relies on the dissociation of solid organs and tissues into high-quality single-cell (or nuclei) suspensions.In this chapter, we examine several key aspects of the tissue handling workflow that need to be considered when establishing an efficient tissue processing protocol for single-cell RNA sequencing (scRNA-seq). These include tissue collection, transport, and storage, as well as the choice of the dissociation conditions. We emphasize the importance of the tissue quality check and discuss the advantages (and potential limitations) of tissue cryopreservation. We provide practical tips and considerations on each of the steps of the processing workflow, and comment on how to maximize cell viability and integrity, which are critical for obtaining high-quality single-cell transcriptomic data.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Single-cell sequencing: promises and challenges for human genetics

Over the last decade, single-cell sequencing has transformed many fields. It has enabled the unbiased molecular phenotyping of even whole organisms with unprecedented cellular resolution. In the field of human genetics, where the phenotypic consequences of genetic and epigenetic alterations are of central concern, this transformative technology promises to functionally annotate every region in the human genome and all possible variants within them at a massive scale. In this review aimed at the clinicians in human genetics, we describe the current status of the field of single-cell sequencing and its role for human genetics, including how the technology works as well as how it is being applied to characterize and monitor diseases, to develop human cell atlases, and to annotate the genome.

Advances in Single-Cell Sequencing Technology and Its Application in Poultry Science

Single-cell sequencing (SCS) uses a single cell as the research material and involves three dimensions: genes, phenotypes and cell biological mechanisms. This type of research can locate target cells, analyze the dynamic changes in the target cells and the relationships between the cells, and pinpoint the molecular mechanism of cell formation. Currently, a common problem faced by animal husbandry scientists is how to apply existing science and technology to promote the production of high-quality livestock and poultry products and to breed livestock for disease resistance; this is also a bottleneck for the sustainable development of animal husbandry. In recent years, although SCS technology has been successfully applied in the fields of medicine and bioscience, its application in poultry science has been rarely reported. With the sustainable development of science and technology and the poultry industry, SCS technology has great potential in the application of poultry science (or animal husbandry). Therefore, it is necessary to review the innovation of SCS technology and its application in poultry science. This article summarizes the current main technical methods of SCS and its application in poultry, which can provide potential references for its future applications in precision breeding, disease prevention and control, immunity, and cell identification.

Connecting past and present: single-cell lineage tracing

Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.

Advances in single-cell sequencing technology in the field of hepatocellular carcinoma

Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.

Single-cell RNA sequencing in atherosclerosis: Mechanism and precision medicine

Atherosclerosis is the pathological basis of various vascular diseases, including those with high mortality, such as myocardial infarction and stroke. However, its pathogenesis is complex and has not been fully elucidated yet. Over the past few years, single-cell RNA sequencing (scRNA-seq) has been developed and widely used in many biological fields to reveal biological mechanisms at the cellular level and solve the problems of cellular heterogeneity that cannot be solved using bulk RNA sequencing. In this review, we briefly summarize the existing scRNA-seq technologies and focus on their application in atherosclerosis research to provide insights into the occurrence, development and treatment of atherosclerosis.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

A companion to the preclinical common data elements for genomics, transcriptomics, and epigenomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. The aim of the CDEs is to improve the standardization of experimental designs across a range of epilepsy research-related methods. Here, we have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for genomics, transcriptomics, and epigenomics in rodent models of epilepsy, with a specific focus on adult rats and mice. We discuss the important elements that need to be considered for genomics, transcriptomics, and epigenomics methodologies, providing a rationale for the parameters that should be collected. This is the first in a two-part series of omics papers with the second installment to cover proteomics, lipidomics, and metabolomics in adult rodents.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Improving Transcriptome Fidelity Following Synovial Tissue Disaggregation

Objective

To improve the fidelity of the cellular transcriptome of disaggregated synovial tissue for applications such as single-cell RNA sequencing (scRNAseq) by modifying the disaggregation technique.

Methods

Osteoarthritis (OA) and rheumatoid arthritis (RA) synovia were collected at arthroplasty. RNA was extracted from intact or disaggregated replicate pools of tissue fragments. Disaggregation was performed with either a proprietary protease, Liberase TL (Lib) as a reference method, Liberase TL with an RNA polymerase inhibitor flavopyridol (Flavo), or a cold digestion with subtilisin A (SubA). qPCR on selected markers and RNAseq were used to compare disaggregation methods using the original intact tissue as reference.

Results

Disaggregated cell yield and viability were similar for all three methods with some viability improved (SubA). Candidate gene analysis showed that Lib alone dramatically increased expression of several genes involved in inflammation and immunity compared with intact tissue and was unable to differentiate RA from OA. Both alternative methods reduced the disaggregation induced changes. Unbiased analysis using bulk RNAseq and the 3 protocols confirmed the candidate gene studies and showed that disaggregation-induced changes were largely prevented. The resultant data improved the ability to distinguish RA from OA synovial transcriptomes.

Conclusions

Disaggregation of connective tissues such as synovia has complex and selective effects on the transcriptome. We found that disaggregation with an RNA polymerase inhibitor or using a cold enzyme tended to limit induction of some relevant transcripts during tissue processing. The resultant data in the disaggregated transcriptome better represented the in situ transcriptome. The specific method chosen can be tailored to the genes of interest and the hypotheses being tested in order to optimize the fidelity of technique for applications based on cell suspensions such as sorted populations or scRNAseq.

Impact of single-cell RNA sequencing on understanding immune regulation

Single‐cell RNA sequencing (scRNA‐seq), one of the most powerful technologies, can describe the transcriptomic heterogeneity of single cells and reveal previously unreported cell types or states in complex tissues. With the rapid development of scRNA‐seq, it has renewed our view of cellular heterogeneity and its significance for deeply understanding cell development and function. There are a large number of studies applying scRNA‐seq to investigate the heterogeneity of immune cells and disease pathogenesis, focusing on differences among every individual cell, which have provided novel inspiration for disease therapy and biological processes. In this review, we describe the development of scRNA‐seq and its application in immune‐related physiological states, regulatory mechanisms and diseases. In addition, we further discuss the opportunities and challenges of scRNA‐seq in immune regulation.

Practical bioinformatics pipelines for single-cell RNA-seq data analysis

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell-cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.

Identification of Five Hub Genes Based on Single-Cell RNA Sequencing Data and Network Pharmacology in Patients With Acute Myocardial Infarction

Acute myocardial infarction (AMI) has a high mortality. The single-cell RNA sequencing (scRNA-seq) method was used to analyze disease heterogeneity at the single-cell level. From the Gene Expression Omnibus (GEO) database (GSE180678), AMI scRNA-seq were downloaded and preprocessed by the Seurat package. Gene expression data came from GSE182923. Cell cluster analysis was conducted. Cell types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed on hub genes. Drugs were predicted by protein–protein interaction (PPI) and molecular docking. In total, 7 cell clusters were defined based on the scRNA-seq dataset, and the clusters were labeled as 5 cell types by marker genes. Hematopoietic stem cell types as a differential subgroups were higher in AMI than in healthy tissues. From available databases and PPI analysis, 52 common genets were identified. Based on 52 genes, 5 clusters were obtained using the MCODE algorithm, and genes in these 5 clusters involved in immune and inflammatory pathways were determined. Correlation analysis showed that hematopoietic stem cell types were negatively correlated with ATM, CARM1, and CASP8 but positively correlated with CASP3 and PPARG. This was reversed with immune cells. Molecular docking analysis showed that DB05490 had the lowest docking score with PPARG. We identified 5 hub genes (ATM, CARM1, CASP8, CASP3, and PPARG) involved in AMI progression. Compound DB05490 was a potential inhibitor of PPAG.

Automatic Microfluidic Cell Wash Platform for Purifying Cells in Suspension: Puriogen

Cell wash is an essential cell sample preparation procedure to eliminate or minimize interfering substances for various subsequent cell analyses. The commonly used cell wash method is centrifugation which separates cells from other biomolecules in a solution with manual pipetting and has many drawbacks such as being labor-intensive and time-consuming with substantial cell loss and cell clumping. To overcome these issues, a centrifuge-free and automatic cell wash platform for cell purity generation, termed Puriogen, has been developed in this work. Compared with other developed products such as AcouWash, Puriogen can process samples with a high throughput of above 500 μL/min. Puriogen utilizes a uniquely designed inertial microfluidic device to complete the automatic cell wash procedure. One single-cell wash procedure with the Puriogen platform can remove more than 90% ambient proteins and nucleic acids from the cell sample. It can also remove most of the residual fluorescent dye after cell staining to significantly reduce the background signals for subsequent cell analysis. By removing the dead cell debris, it can increase the live cell percentage in the sample by 2-fold. Moreover, the percentage of single-cell population is also increased by 20% because of further disassociation of small-cell aggregates (e.g., doublets and triplets) into singlets. To freely adjust cell concentrations, the Puriogen platform can concentrate cells 5 times in a single flow-through process. The presented Puriogen cell wash solution has broad applications in cell preparation with its excellent simplicity in operation and wash efficiency, especially in single-cell sequencing.

2021 White Paper on Recent Issues in Bioanalysis: ISR for Biomarkers, Liquid Biopsies, Spectral Cytometry, Inhalation/Oral & Multispecific Biotherapeutics, Accuracy/LLOQ for Flow Cytometry (Part 2 - Recommendations on Biomarkers/CDx Assays Development & Validation, Cytometry Validation & Innovation, Biotherapeutics PK LBA Regulated Bioanalysis, Critical Reagents & Positive Controls Generation)

The 15th edition of the Workshop on Recent Issues in Bioanalysis (15th WRIB) was held on 27 September to 1 October 2021. Even with a last-minute move from in-person to virtual, an overwhelmingly high number of nearly 900 professionals representing pharma and biotech companies, contract research organizations (CROs), and multiple regulatory agencies still eagerly convened to actively discuss the most current topics of interest in bioanalysis. The 15th WRIB included three Main Workshops and seven Specialized Workshops that together spanned 1 week in order to allow exhaustive and thorough coverage of all major issues in bioanalysis, biomarkers, immunogenicity, gene therapy, cell therapy and vaccines. Moreover, in-depth workshops on biomarker assay development and validation (BAV) (focused on clarifying the confusion created by the increased use of the term "context of use" [COU]); mass spectrometry of proteins (therapeutic, biomarker and transgene); state-of-the-art cytometry innovation and validation; and critical reagent and positive control generation were the special features of the 15th edition. This 2021 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2021 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 2) covers the recommendations on ISR for Biomarkers, Liquid Biopsies, Spectral Cytometry, Inhalation/Oral & Multispecific Biotherapeutics, Accuracy/LLOQ for Flow Cytometry. Part 1A (Endogenous Compounds, Small Molecules, Complex Methods, Regulated Mass Spec of Large Molecules, Small Molecule, PoC), Part 1B (Regulatory Agencies' Inputs on Bioanalysis, Biomarkers, Immunogenicity, Gene & Cell Therapy and Vaccine) and Part 3 (TAb/NAb, Viral Vector CDx, Shedding Assays; CRISPR/Cas9 & CAR-T Immunogenicity; PCR & Vaccine Assay Performance; ADA Assay Comparability & Cut Point Appropriateness) are published in volume 14 of Bioanalysis, issues 9 and 11 (2022), respectively.