High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq

Epithelioid Hemangioendothelioma: Treatment Landscape and Innovations for an Ultra-Rare Sarcoma

OPINION STATEMENT

Epithelioid hemangioendothelioma (EHE) is an ultra-rare sarcoma with a paucity of data on best practices for management. Pathogenic translocations involving the YAP or TAZ genes lead to constitutive activation of TEAD and TEAD-associated pathways. As our understanding of the molecular drivers of EHE has advanced, investigational treatment strategies have shifted away from cytotoxic chemotherapy toward more targeted approaches. This review focuses on the historical context and evolving landscape of systemic therapies for patients with EHE. For newly diagnosed patients, we recommend consultation at a high-volume sarcoma center whenever possible. If the disease is localized and resectable, surgical excision by a sarcoma-focused surgical oncologist is preferred. When the disease is unresectable, we first assess for disease progression to determine whether active surveillance is appropriate. Some patients may experience indolent, asymptomatic disease for years-or even decades-without requiring intervention. In patients with progressive or symptomatic unresectable disease, systemic therapy is warranted. Setting realistic expectations about the goals of treatment is essential, as no current systemic therapies reliably reduce tumor burden. However, molecular profiling and ongoing correlative studies from clinical trials may soon identify more effective therapeutic targets. For this reason, we encourage referral to centers that routinely perform molecular profiling and offer clinical trials with eligibility criteria for EHE, even to be considered as a first-line approach. Outside of a clinical trial, cytotoxic chemotherapy remains the frontline standard of care for patients who require systemic treatment. Importantly, treatment decisions must incorporate patient preferences and recognition that symptomatic improvement alone can be a meaningful outcome for preserving quality of life.

Single microorganism RNA sequencing of microbiomes using smRandom-Seq

Bacteria colonize nearly every part of the human body and various environments, displaying remarkable diversity. Traditional population-level transcriptomics measurements provide only average population behaviors, often overlooking the heterogeneity within bacterial communities. To address this limitation, we have developed a droplet-based, high-throughput single-microorganism RNA sequencing method (smRandom-seq) that offers highly species specific and sensitive gene detection. Here we detail procedures for microbial sample preprocessing, in situ preindexed cDNA synthesis, in situ poly(dA) tailing, droplet barcoding, ribosomal RNA depletion and library preparation. The main smRandom-seq workflow, including sample processing, in situ reactions and library construction, takes ~2 days. This method features enhanced RNA coverage, reduced doublet rates and minimized ribosomal RNA contamination, thus enabling in-depth analysis of microbial heterogeneity. smRandom-seq is compatible with microorganisms from both laboratory cultures and complex microbial community samples, making it well suited for constructing single-microorganism transcriptomic atlases of bacterial strains and diverse microbial communities. This Protocol requires experience in molecular biology and RNA sequencing techniques, and it holds promising potential for researchers investigating bacterial resistance, microbiome heterogeneity and host-microorganism interactions.

The Curated Cancer Cell Atlas provides a comprehensive characterization of tumors at single-cell resolution

Recent years have seen a rapid proliferation of single-cell cancer studies, yet most of these studies profiled few tumors, limiting their statistical power. Combining data and results across studies holds great promise but also involves various challenges. We recently began to address these challenges by curating a large collection of cancer single-cell RNA-sequencing datasets, leveraging it for systematic analyses of tumor heterogeneity. Here we greatly extend this repository to 124 datasets for over 40 cancer types, together comprising 2,836 samples, with improved data annotations, visualizations and exploration. Using this vast cohort, we generate an updated map of recurrent expression programs in malignant cells and systematically quantify context-dependent gene expression and cell-cycle patterns across cell types and cancer types. These data, annotations and analysis results are all freely available for exploration and download through the Curated Cancer Cell Atlas, a central community resource that opens new avenues in cancer research.

snPATHO-seq: A Detailed Protocol for Single Nucleus RNA Sequencing From FFPE

Formalin-fixed paraffin-embedded (FFPE) samples remain an underutilized resource in single-cell omics due to RNA degradation from formalin fixation. Here, we present snPATHO-seq, a robust and adaptable approach that enables the generation of high-quality single-nucleus (sn) transcriptomic data from FFPE tissues, utilizing advancements in single-cell genomic techniques. The snPATHO-seq workflow integrates optimized nuclei isolation with the 10× Genomics Flex assay, targeting short RNA fragments to mitigate FFPE-related RNA degradation. Benchmarking against standard 10× 3' and Flex assays for fresh/frozen tissues confirmed robust detection of transcriptomic signatures and cell types. snPATHO-seq demonstrated high performance across diverse FFPE samples, including diseased tissues like breast cancer. It seamlessly integrates with FFPE spatial transcriptomics (e.g., FFPE Visium) for multi-modal spatial and single-nucleus profiling. Compared to workflows like 10× Genomics' snFFPE, snPATHO-seq delivers superior data quality by reducing tissue debris and preserving RNA integrity via nuclei isolation. This cost-effective workflow enables high-resolution transcriptomics of archival FFPE samples, advancing single-cell omics in translational and clinical research. Key features • Optimized nuclei isolation from FFPE tissues enables high-quality single-nucleus transcriptomics by minimizing debris and maximizing intact nuclear yield. • Compatible with 10× Genomics Flex, leveraging short RNA probes to overcome FFPE RNA fragmentation challenges. • Outperforms existing FFPE workflows in cell type detection sensitivity across archival, degraded, or aged samples. • Low-cost, accessible protocol using off-the-shelf reagents, suitable for broad translational and archival tissue applications.

snCED-seq: high-fidelity cryogenic enzymatic dissociation of nuclei for single-nucleus RNA-seq of FFPE tissues

Recent advances have shown that single-nucleus RNA sequencing (snRNA-seq) can be applied to formalin-fixed, paraffin-embedded (FFPE) tissues, opening avenues for transcriptomic analysis of archived specimens. Yet, isolating intact nuclei remains difficult due to RNA cross-linking. Here, we introduce a cryogenic enzymatic dissociation (CED) strategy for rapid, high-yield and fidelity nuclei extraction from FFPE samples and validate its utility with snRandom-seq (snCED-seq) using male C57/BL6 mice. Compared with conventional approaches, CED delivers a tenfold increase in nuclei yield with significantly reduced hands-on time, while minimizing secondary RNA degradation and preserving intranuclear transcripts. snCED-seq enhances gene detection sensitivity, lowers mitochondrial and ribosomal contamination, and increases overall gene expression quantification. In Alzheimer's disease studies, it distinguished two astrocyte subpopulations, microglia, and oligodendrocytes, revealing cellular heterogeneity. Additionally, snCED-seq identify major cell types in a single 50 μm FFPE human lung section. Our results demonstrate that snCED-seq is robust for FFPE specimens and poised to enable multi-omics analyses of clinical samples.

Circular RNA discovery with emerging sequencing and deep learning technologies

Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disease pathogenesis. However, their low expression levels and high sequence similarity to linear RNAs present substantial challenges for circRNA detection and characterization. Recent advances in long-read and single-cell RNA sequencing technologies, coupled with sophisticated deep learning-based algorithms, have revolutionized the investigation of circRNAs at unprecedented resolution and scale. This Review summarizes recent breakthroughs in circRNA discovery, characterization and functional analysis algorithms. We also discuss the challenges associated with integrating large-scale circRNA sequencing data and explore the potential future development of artificial intelligence (AI)-driven algorithms to unlock the full potential of circRNA research in biomedical applications.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Single-nucleus total RNA sequencing of formalin-fixed paraffin-embedded samples using snRandom-seq

Formalin-fixed paraffin-embedded (FFPE) samples represent a vast and valuable resource of patient material, often linked to extensive clinical history and follow-up data. However, achieving single-cell or single-nucleus RNA (sc/snRNA) profiling in these archived tissues remains challenging. To address this, we have developed snRandom-seq, a droplet- and random primer-based single-nucleus total RNA sequencing technology specifically designed for FFPE tissues. This method captures total RNAs by using random primers and demonstrates a low doublet rate (0.3%), increased RNA coverage and enhanced detection of non-coding and nascent RNAs compared to state-of-the-art high-throughput sc/snRNA-seq technologies. This protocol provides a comprehensive guide to isolating single nuclei from FFPE samples; performing in situ DNA blocking, reverse transcription and dA tailing reactions; barcoding single-nucleus droplets; and preparing sequencing libraries. The entire snRandom-seq process can be completed in 4 d. This platform serves as a powerful tool for snRNA-seq of clinical specimens, with broad applications in studying complex biological systems.

Spatial multi-omics profiling of breast cancer oligo-recurrent lung metastasis

Primary breast cancer (BC) and metastatic tumors exhibit distinct tumor microenvironment (TME) ecosystems, and the heterogeneity of the TME of BC poses challenges to effective therapies. Evaluating the TME at the single-cell and spatial profiles offers potential for more precise treatments. However, due to the challenge of obtaining surgical specimens of both primary BC and oligo-recurrent lung metastasis simultaneously for high-resolution spatial analysis, the TME of lung-specific metastases using paired samples remains largely unexplored. In this study, we developed a comprehensive strategy using imaging mass cytometry (IMC), spatial proteomics, single-nucleus RNA-seq (snRNA-seq) and multiplex immunofluorescence to explore the spatial topology of lung-specific metastasis and the underlying biological mechanisms based on formalin-fixed paraffin-embedded (FFPE) samples from BC and paired lung metastasis. A total of 250,600 high-quality cells with spatial information revealed by IMC depicted the spatial differences in the TME between BC and lung metastasis. A significant increase in HLA-DR+ epithelial cells, endothelial cells and exhausted T cells was detected in lung metastases compared to primary sites, with this difference accentuated in the triple-negative subtype. Moreover, a distinct cellular hub comprising endothelial cells and HLA-DR+ epithelial cells implies the potential promising effect of anti-angiogenic therapy and immunotherapy in BC with lung metastasis, which was further validated by multiplex immunofluorescence analysis. Spatial proteomics further explored the underlying mechanism of TME components identified by IMC analysis. snRNA-seq validated the enrichment of endothelial cells in lung metastasis than that in BC at a whole FFPE slide level. In conclusion, this study determines the spatial multi-omics profiling of TME components at a single-cell resolution using paired samples of primary BC and lung oligo-metastasis. The comprehensive analysis may contribute to the development of therapeutic options.

Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data with miRTalk

MicroRNAs are released from cells in extracellular vesicles (EVs), representing an essential mode of cell-cell communication (CCC) via a regulatory effect on gene expression. Single-cell RNA-sequencing technologies have ushered in an era of elucidating CCC at single-cell resolution. Herein, we present miRTalk, a pioneering approach for inferring CCC mediated by EV-derived miRNA-target interactions (MiTIs). The benchmarking against simulated and real-world datasets demonstrates the superior performance of miRTalk, and the application to four disease scenarios reveals the in-depth MiTI-mediated CCC mechanisms. Collectively, miRTalk can infer EV-derived MiTI-mediated CCC with scRNA-seq data, providing new insights into the intercellular dynamics of biological processes.

Chromosomal Instability and Clonal Heterogeneity in Breast Cancer: From Mechanisms to Clinical Applications

BACKGROUND

Chromosomal instability (CIN) and clonal heterogeneity (CH) are fundamental hallmarks of breast cancer that drive tumor evolution, disease progression, and therapeutic resistance. Understanding the mechanisms underlying these phenomena is essential for improving cancer diagnosis, prognosis, and treatment strategies.

METHODS

In this review, we provide a comprehensive overview of the biological processes contributing to CIN and CH, highlighting their molecular determinants and clinical relevance.

RESULTS

We discuss the latest advances in detection methods, including single-cell sequencing and other high-resolution techniques, which have enhanced our ability to characterize intratumoral heterogeneity. Additionally, we explore how CIN and CH influence treatment responses, their potential as therapeutic targets, and their role in shaping the tumor immune microenvironment, which has implications for immunotherapy effectiveness.

CONCLUSIONS

By integrating recent findings, this review underscores the impact of CIN and CH on breast cancer progression and their translational implications for precision medicine.

MICRORNA-146B TARGETS HIF-1Α AND ATTENUATES CARDIOMYOCYTE APOPTOSIS AND FIBROSIS IN DOXORUBICIN-INDUCED HEART FAILURE

ABSTRACT

The global prevalence of heart failure is still growing, which imposes a heavy economic burden. The role of microRNA-146b (miR-146b) in HF remains largely unknown. This study aims to explore the role and mechanism of miR-146b in HF. Method: We applied reverse transcription-polymerase chain reaction to search for differential microRNAs between myocardial tissues of heart failure patients and controls. We also used reverse transcription-polymerase chain reaction to detect the miR-146b expression in primary neonatal mouse cardiomyocytes and mice models of doxorubicin-induced HF. In vivo experiments, echocardiography was performed at baseline and weeks 6. After that we harvested mice's heart and evaluated the cardiomyocyte with hematoxylin and eosin (HE), Masson trichrome staining, and TUNEL staining. Through bioinformatics analysis, we found HIF-1α might be the target gene of miR-146b, which validated by luciferase reporter gene assay. Subsequently, mRNA and protein expression levels of HIF-1α were detected by overexpression or inhibition of miR-146b in primary neonatal mouse cardiomyocytes. Results: We found that miR-146b expression was decreased in myocardial tissues of HF patients compared with controls ( P < 0.01). MiR-146b levels were notably downregulated in HF models. MiR-146b knockout mice showed a more pronounced decrease in cardiac function and more severe myocardial fibrosis and apoptosis than wild type. Meanwhile, over expression or repression of miR-146b in primary neonatal mouse cardiomyocytes could inhibit or upregulate HIF-1α mRNA and protein expression. Conclusion : Our study shows that miR-146b may be a protective factor for cardiomyocytes by modulating HIF-1α.

High resolution tissue and cell type identification via single cell transcriptomic profiling

Tissue identification can be instrumental in reconstructing a crime scene but remains a challenging task in forensic investigations. Conventionally, identifying the presence of certain tissue from tissue mixture by predefined cell type markers in bulk fashion is challenging due to limitations in sensitivity and accuracy. In contrast, single-cell RNA sequencing (scRNA-Seq) is a promising technology that has the potential to enhance or even revolutionize tissue and cell type identification. In this study, we developed a high sensitive general purpose single cell annotation pipeline, scTissueID, to accurately evaluate the single cell profile quality and precisely determine the cell and tissue types based on scRNA profiles. By incorporating a crucial and unique reference cell quality differentiation phase of targeting only high confident cells as reference, scTissueID achieved better and consistent performance in determining cell and tissue types compared to 8 state-of-art single cell annotation pipelines and 6 widely adopted machine learning algorithms, as demonstrated through a large-scale and comprehensive comparison study using both forensic-relevant and Human Cell Atlas (HCA) data. We highlighted the significance of cell quality differentiation, a previously undervalued factor. Thus, this study offers a tool capable of accurately and efficiently identifying cell and tissue types, with broad applicability to forensic investigations and other biomedical research endeavors.

High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-world situations and promises new perspectives in the understanding of human microbiomes.

Single-Nucleus RNA-Sequencing Reveals a MET+ Oligodendrocyte Subpopulation That Promotes Proliferation of Radiation-Induced Gliomas

PURPOSE

Radiation-induced gliomas (RIGs) are fatal late complications of radiation therapy, with a median survival time of 6 to 11 months. RIGs demonstrate a unique molecular landscape and may originate from a glial lineage distinct from that of primary malignancies or diffuse midline gliomas (DMGs). This study aimed to explore the intratumoral diversity within RIGs to uncover their cellular origin and characteristics and enhance our understanding of this uncommon tumor type.

METHODS AND MATERIALS

Formalin-fixed, paraffin-embedded samples were collected from 2 RIGs and 2 DMGs for single-nucleus RNA sequencing. A detailed analysis was conducted to assess intratumoral heterogeneity and cellular interactions, including gene set enrichment, pseudotime trajectory, and cell communication analyses. Immunofluorescence staining, proliferation assay, and RNA-seq analysis were also applied to validate our findings.

RESULTS

Our analysis revealed distinct heterogeneity in oligodendrocytes (ODs) between the DMG and RIG samples. A unique subpopulation of ODs in RIGs, which was characterized by gene encoding mesenchymal-epithelial transition factor (MET), and therefore termed MET+ ODs, exhibited characteristics typical of cancer cells, such as increased mitotic activity, cancer-related gene expression, and extensive copy number variations. Cell communication studies indicated that MET+ ODs interact vigorously with G1/S and G2/M cycling cells via the neural cell adhesion molecule signaling pathway, potentially enhancing the proliferation of cycling malignant cells. Integrating our results with existing RNA-seq data further supported our hypothesis. The presence of MET+ ODs in RIGs was confirmed by immunostaining, and activation of the neural cell adhesion molecule signaling pathway in vitro significantly promoted the proliferation of RIG tumor cells. Moreover, in vitro radiation induced the transformation of ODs to be more similar to MET+ ODs.

CONCLUSIONS

RIGs are characterized by an OD composition distinct from that of DMGs. A specific subpopulation of MET+ ODs in RIGs may be crucial in tumorigenesis and promote the growth of malignant cells. Identifying MET+ ODs offers a valuable target for future clinical surveillance and therapeutic strategies.

A cost-effective and scalable approach for DNA extraction from FFPE tissues

Genomic profiling of cancer plays an increasingly vital role for diagnosis and therapy planning. In addition, research of novel diagnostic applications such as DNA methylation profiling requires large training and validation cohorts. Currently, most diagnostic cases processed in pathology departments are stored as formalin-fixed and paraffin embedded tissue blocks (FFPE). Consequently, there is a growing demand for high-throughput extraction of nucleic acids from FFPE tissue samples. While proprietary kits are available, they are expensive and offer little flexibility. Here, we present ht-HiTE, a high-throughput implementation of a recently published and highly efficient DNA extraction protocol. This approach enables manual and automated processing of 96-well plates with a liquid handler, offers two options for purification and utilizes off-the-shelf reagents. Finally, we show that NGS and DNA methylation microarray data obtained from DNA processed with ht-HiTE are of equivalent quality as compared to a manual, kit-based approach.

Identification of cell-type specificity, trans- and cis-acting functions of plant lincRNAs from single-cell transcriptomes

Long noncoding RNAs, including intergenic lncRNAs (lincRNAs), play a key role in various biological processes throughout the plant life cycle, and the advent of single-cell RNA sequencing (scRNA-seq) technology has opened up a valuable avenue for scrutinizing the intricate roles of lincRNAs in cellular processes. Here, we identified a new batch of lincRNAs using scRNA-seq data from diverse tissues of plants (rice, Arabidopsis, tomato, and maize). Based on well-annotated single-cell transcriptome atlases, plant lincRNAs were found to possess the same level of cell-type specificity as mRNAs and to be involved in the differentiation of certain cell types based on pseudo-time analysis. Many lincRNAs were predicted to play a hub role in the cell-type-specific co-expression networks of lincRNAs and mRNAs, suggesting their trans-acting abilities. Besides, plant lincRNAs were revealed to have potential cis-acting properties based on their genomic distances and expression correlations with the neighboring mRNAs. Furthermore, an online platform, PscLncRNA (http://ibi.zju.edu.cn/psclncrna/), was constructed for searching and visualizing all identified plant lincRNAs with annotated potential functions. Our work provides new insights into plant lincRNAs at single-cell resolution and an important resource for understanding and further investigation of plant lincRNAs.

High-Throughput Single-Nucleus RNA Profiling of Minimal Puncture FFPE Samples Reveals Spatiotemporal Heterogeneity of Cancer

Puncture biopsy, especially those preserved by formalin fixed paraffin embedding (FFPE) samples, play an important role in various research purposes. Diverse single-nucleus RNA sequencing (snRNA-seq) techniques have been developed for FFPE samples, however, how to perform high-throughput snRNA-seq on small FFPE puncture samples is still a challenge. Here, the previously developed snRNA-seq technique (snRandom-seq) is optimized by implementing a pre-indexing procedure for the minimal puncture FFPE samples. In analyzing 20 samples from various solid tumors, optimized snRandom-seq still detected ≈17 000 genes and 12 000 long non-coding RNAs (lncRNAs), achieving precise clustering based on tissue origin. A head-to-head comparison with 10× Genomics on fresh biopsy samples showed a similar gene detection rate, with significantly enhanced lncRNA detection, indicating that the optimized snRandom-seq technique maintains its established gene detection advantages even when applied to small samples. Utilizing 7 puncture FFPE samples of liver metastases from 3 colorectal cancer patients pre- and post-immunotherapy, the cellular developmental trajectories are reconstructed and revealed dynamic spatiotemporal heterogeneity during treatment, including insights into pseudoprogression of immunotherapy. Therefore, the optimized snRandom-seq offers a solution for high-throughput single-cell RNA and non-coding RNA analysis in minimal puncture FFPE sample.

snRNA-seq of long-preserved FFPE samples from colorectal liver metastasis lesions with diverse prognoses

Differences in prognostic outcomes are prevalent in patients with colorectal cancer liver metastases. Comparative analysis of tissue samples, particularly applying single-cell transcriptome sequencing technology, can provide a deeper understanding of potential impacting factors. However, long-term monitoring for prognosis determination necessitates extended preservation of tissue samples using formalin-fixed and paraffin-embedded (FFPE) treatments, which can cause substantial RNA degradation, presenting challenges to single-cell or single-nucleus sequencing. In this study, employing snRandom-seq, a single-nucleus RNA sequencing (snRNA-seq) technology specifically for FFPE samples, we tested multiple lesion samples from 18 distinctive colorectal cancer liver metastasis cases with diverse prognostic outcomes that have been preserved for at least three years (mostly over five years). The process yielded expression data from 82,285 cells. The high-quality snRNA-seq data demonstrate the feasibility of single-nucleus sequencing in long-term preserved FFPE samples, offering potential insights into the heterogeneity between different prognoses of colorectal cancer liver metastases, and the relationship between the heterogeneity within different lesions of the same patient and prognosis.

Protocol for enhancing RNA yield and quality from single nuclei isolated from mouse brain tissue

Isolating RNA from single nuclei is essential for single-cell gene expression analysis, yet obtaining high-quality RNA is challenging. We present a protocol to enhance RNA yield and quality from mouse brain nuclei. Key steps include brain dissection, thawing, homogenization, and centrifugation-based isolation. The protocol incorporates 3% glyoxal fixation for RNA preservation, followed by filtration, blocking, and fluorescence-activated sorting to ensure the extracted RNA meets quality and quantity standards for transcriptomic and qPCR analyses. For complete details on the use and execution of this protocol, please refer to Marin-Valencia, Kocabas et al.1.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

Time-resolved single-cell transcriptomic sequencing

Cells experience continuous transformation under both physiological and pathological circumstances. Single-cell RNA sequencing (scRNA-seq) is competent in disclosing the disparities of cells; nevertheless, it poses challenges in linking the individual cell state at distinct time points. Although computational approaches based on scRNA-seq data have been put forward for trajectory analysis, the result is based on assumptions and fails to reflect the actual states. Consequently, it is necessary to incorporate a "time anchor" into the scRNA-seq library for the temporal documentation of the dynamic expression pattern. This review comprehensively overviews the time-resolved single-cell transcriptomic sequencing methodologies and applications. As scRNA-seq functions as the basis for profiling single-cell expression patterns, the review initially introduces various scRNA-seq approaches. Subsequently, the review focuses on the different experimental strategies for introducing a "time anchor" to scRNA-seq, highlighting their principles, strengths, weaknesses, and comparing their adaptation in various scenarios. Next, it provides a brief summary of applications in immunity response, cancer progression, and embryo development. Finally, the review concludes with a forward-looking perspective on future advancements in time-resolved single-cell transcriptomic sequencing.

snPATHO-seq, a versatile FFPE single-nucleus RNA sequencing method to unlock pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low RNA quality. In this study, leveraging a recent advance in single-cell genomic technology, we introduce snPATHO-seq, a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples. We benchmarked the performance of the snPATHO-seq workflow against existing 10x 3' and Flex assays designed for frozen or fresh samples and highlighted the consistency in snRNA-seq data produced by all workflows. The snPATHO-seq workflow also demonstrated high robustness when tested across a wide range of healthy and diseased FFPE tissue samples. When combined with FFPE spatial transcriptomic technologies such as FFPE Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE samples, allowing more comprehensive transcriptomic characterization.

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Characterization, Epitope Confirmation, and Cross-Reactivity Analysis of Parvalbumin from Lateolabrax maculatus by Multiomics Technologies

Spotted seabass (Lateolabrax maculatus) is the second largest maricultural fish species in China and is the main trigger of food-related allergic reactions. Nevertheless, studies on the allergens of L. maculatus are limited. This study aimed to characterize pan-allergen parvalbumin from L. maculatus. Two proteins of about 11 kDa were purified and confirmed as parvalbumins by mass spectrometry. The IgG- and IgE-binding activities were evaluated through an immunoblotting assay. The molecular characteristics of β-parvalbumin were investigated by combining proteomics, genomics, and immunoinformatics approaches. The results indicated that β-parvalbumin consists of 109 amino acids with a molecular weight of 11.5 kDa and is the major allergen displaying strong IgE-binding capacity. In silico analysis and a dot blotting assay confirmed seven linear B cell epitopes distributed mainly on α-helixes and the calcium-binding loops. In addition, the cross-reactivity among 26 commonly consumed fish species was analyzed. The in-house generated anti-L. maculatus parvalbumin polyclonal antibody recognized 100% of the 26 fish species, demonstrating cross-reactivity and better binding capacity than the anticod parvalbumin antibody. Together, this study provides an efficient protocol to characterize allergens with multiomics methods and supports parvalbumin from L. maculatus as a candidate for fish allergen determination and allergy diagnosis.

Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors

Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

hadge: a comprehensive pipeline for donor deconvolution in single-cell studies

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.

High-Throughput Host-Microbe Single-Cell RNA Sequencing Reveals Ferroptosis-Associated Heterogeneity during Acinetobacter baumannii Infection

Interactions between host and bacterial cells are integral to human physiology. The complexity of host-microbe interactions extends to different cell types, spatial aspects, and phenotypic heterogeneity, requiring high-resolution approaches to capture their full complexity. The latest breakthroughs in single-cell RNA sequencing (scRNA-seq) have opened up a new era of studies in host-pathogen interactions. Here, we first report a high-throughput cross-species dual scRNA-seq technology by using random primers to simultaneously capture both eukaryotic and bacterial RNAs (scRandom-seq). Using reference cells, scRandom-seq can detect individual eukaryotic and bacterial cells with high throughput and high specificity. Acinetobacter baumannii (A.b) is a highly opportunistic and nosocomial pathogen that displays resistance to many antibiotics, posing a significant threat to human health, calling for discoveries and treatment. In the A.b infection model, scRandom-seq witnessed polarization of THP-1 derived-macrophages and the intracellular A.b-induced ferroptosis-stress in host cells. The inhibition of ferroptosis by Ferrostatin-1 (Fer-1) resulted in the improvement of cell vitality and resistance to A.b infection, indicating the potential to resist related infections. scRandom-seq provides a high-throughput cross-species dual single-cell RNA profiling tool that will facilitate future discoveries in unraveling the complex interactions of host-microbe interactions in infection systems and tumor micro-environments.

Breaks for Precision Medicine in Cancer: Development and Prospects of Spatiotemporal Transcriptomics

With the development of the social economy and the deepening understanding of cancer, cancer has become a significant cause of death, threatening human health. Although researchers have made rapid progress in cancer treatment strategies in recent years, the overall survival of cancer patients is still not optimistic. Therefore, it is essential to reveal the spatial pattern of gene expression, spatial heterogeneity of cell populations, microenvironment interactions, and other aspects of cancer. Spatiotemporal transcriptomics can help analyze the mechanism of cancer occurrence and development, greatly help precise cancer treatment, and improve clinical prognosis. Here, we review the integration strategies of single-cell RNA sequencing and spatial transcriptomics data, summarize the recent advances in spatiotemporal transcriptomics in cancer studies, and discuss the combined application of spatial multiomics, which provides new directions and strategies for the precise treatment and clinical prognosis of cancer.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer

Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.

Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology

The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Heat Inactivation of Nipah Virus for Downstream Single-Cell RNA Sequencing Does Not Interfere with Sample Quality

Single-cell RNA sequencing (scRNA-seq) technologies are instrumental to improving our understanding of virus-host interactions in cell culture infection studies and complex biological systems because they allow separating the transcriptional signatures of infected versus non-infected bystander cells. A drawback of using biosafety level (BSL) 4 pathogens is that protocols are typically developed without consideration of virus inactivation during the procedure. To ensure complete inactivation of virus-containing samples for downstream analyses, an adaptation of the workflow is needed. Focusing on a commercially available microfluidic partitioning scRNA-seq platform to prepare samples for scRNA-seq, we tested various chemical and physical components of the platform for their ability to inactivate Nipah virus (NiV), a BSL-4 pathogen that belongs to the group of nonsegmented negative-sense RNA viruses. The only step of the standard protocol that led to NiV inactivation was a 5 min incubation at 85 °C. To comply with the more stringent biosafety requirements for BSL-4-derived samples, we included an additional heat step after cDNA synthesis. This step alone was sufficient to inactivate NiV-containing samples, adding to the necessary inactivation redundancy. Importantly, the additional heat step did not affect sample quality or downstream scRNA-seq results.

Construction of multiplexed transcriptome NGS libraries of microdissected tissue samples based on combinational DNA barcode microbeads

The combination of single-cell RNA sequencing and microdissection techniques that preserves positional information has become a major tool for spatial transcriptome analyses. However, high costs and time requirements, especially for experiments at the single cell scale, make it challenging for this approach to meet the demand for increased throughput. Therefore, we proposed combinational DNA barcode (CDB)-seq as a medium-throughput, multiplexed approach combining Smart-3SEQ and CDB magnetic microbeads for transcriptome analyses of microdissected tissue samples. We conducted a comprehensive comparison of conditions for CDB microbead preparation and related factors and then applied CDB-seq to RNA extracts, fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) mouse brain tissue samples. CDB-seq transcriptomic profiles of tens of microdissected samples could be obtained in a simple, cost-effective way, providing a promising method for future spatial transcriptomics.

Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.

Dynamic profiling of immune microenvironment during anti-PD-1 immunotherapy for head and neck squamous cell carcinoma: the IPRICE study

BACKGROUND

Immune checkpoint inhibitors of programmed cell death protein 1 (PD-1) represent a significant breakthrough in treating head and neck squamous cell carcinoma (HNSCC), with long-lasting responses and prolonged survival observed in first- and second-line therapy. However, this is observed in < 20% of patients and high primary/secondary resistance may occur. The primary objective of the identification of predictive factors for the response to anti-PD-1 immunotherapy in head and neck squamous cell carcinoma (IPRICE) study is to identify predictive factors of response to anti-PD-1 immunotherapy.

METHODS

The IPRICE study is a single-center, prospective, non-randomized, open-label, and interventional clinical trial. Liquid and tumor biopsies will be performed in 54 patients with recurrent/metastatic (R/M) HNSCC undergoing anti-PD-1 immunotherapy alone to compare the evolution of gene expression and immunological profile between responders and non-responders. We will use a multidisciplinary approach including spatial transcriptomics, single seq-RNA analysis, clinical data, and medical images. Genes, pathways, and transcription factors potentially involved in the immune response will also be analyzed, including genes involved in the interferon-gamma (IFN-γ) pathway, immunogenic cell death and mitophagy, hypoxia, circulating miRNA-mediated immunomodulation, cytokines, and immune repertoire within the tumor microenvironment (TME). With a follow-up period of 3-years, these data will help generate effective biomarkers to define optimal therapeutic strategy and new immunomodulatory agents based on a better understanding of primary/secondary resistance mechanisms. Tumor biopsy will be performed initially before the start of immunotherapy at the first tumor assessment and is only proposed at tumor progression. Clinical data will be collected using a dedicated Case Report Form (CRF).

DISCUSSION

Identifying predictive factors of the response to anti-PD-1 immunotherapy and optimizing long-term immune response require a thorough understanding of the intrinsic and acquired resistance to immunotherapy. To achieve this, dynamic profiling of TME during anti-PD-1 immunotherapy based on analysis of tumor biopsy samples is critical. This will be accomplished through the anatomical localization of HNSCC, which will allow for the analysis of multiple biopsies during treatment and the emergence of breakthrough technologies including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics.

TRIAL REGISTRATION

Clinicaltrial.gov. Registered April 14, 2022, https://www.

CLINICALTRIALS

gov/study/NCT05328024 .

A roadmap for delivering a human musculoskeletal cell atlas

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims to create a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

Single-Nucleus RNA-Seq: Open the Era of Great Navigation for FFPE Tissue

Single-cell sequencing (scRNA-seq) has revolutionized our ability to explore heterogeneity and genetic variations at the single-cell level, opening up new avenues for understanding disease mechanisms and cell-cell interactions. Single-nucleus RNA-sequencing (snRNA-seq) is emerging as a promising solution to scRNA-seq due to its reduced ionized transcription bias and compatibility with richer samples. This approach will provide an exciting opportunity for in-depth exploration of billions of formalin-fixed paraffin-embedded (FFPE) tissues. Recent advancements in single-cell/nucleus gene expression workflows tailored for FFPE tissues have demonstrated their feasibility and provided crucial guidance for future studies utilizing FFPE specimens. In this review, we provide a broad overview of the nuclear preparation strategies, the latest technologies of snRNA-seq applicable to FFPE samples. Finally, the limitations and potential technical developments of snRNA-seq in FFPE samples are summarized. The development of snRNA-seq technologies for FFPE samples will lay a foundation for transcriptomic studies of valuable samples in clinical medicine and human sample banks.