Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows

Spatial genomics: Mapping the landscape of fibrosis

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

Portable and rapid solid sample preparation system utilizing twin-screw mechanism for diagnostic applications

Solid specimens play a crucial role in diagnostic and analytical testing, yet their integration into in vitro diagnostics (IVD) is often limited by lengthy processing times and bulky sample preparation equipment. In this study, we introduce a novel twin-screw mechanical maceration system that enables rapid, continuous, and efficient solid sample preparation within a compact portable platform. By utilizing counter-rotating twin screws, the system generates high shear forces, significantly reducing processing time while maintaining high sample recovery efficiency. We validated its versatility across diverse solid sample types, demonstrating efficient bacterial elution from plant tissues and single-cell dissociation of animal tissues. Our device achieved bacterial elution from plant samples in under 1 min, which is 30 times faster than conventional stomaching, while maintaining a significantly smaller footprint. For animal tissue samples, it dissociated tissue samples of varying sizes (5 g to 100 mg) into single-cell suspensions within 1 min. Furthermore, we explored scalability with a miniaturized device fabricated using 3D printing, which retained comparable performance while reducing volume requirements, expediting processing time, and enabling manual operation without an external power source. This rapid, compact, adaptable, and highly efficient twin-screw system outperforms conventional solid sample processing techniques, making it a promising innovation for a wide range of biomedical applications, from point-of-care diagnostics to tissue biopsies, food hygiene, and agricultural monitoring.

Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex

Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefrontal cortex from 22 tissue blocks, including bulk RNA-seq, reference snRNA-seq, and orthogonal measurement of cell type proportions with RNAScope/ImmunoFluorescence. We use this dataset to evaluate six deconvolution algorithms. Bisque and hspe were the most accurate methods. The dataset, as well as the Mean Ratio gene marker finding method, is made available in the DeconvoBuddies R/Bioconductor package.

Repertoire-based mapping and time-tracking of T helper cell subsets in scRNA-Seq

Introduction

The functional programs of CD4+ T helper (Th) cell clones play a central role in shaping immune responses to different challenges. While advances in single-cell RNA sequencing (scRNA-Seq) have significantly improved our understanding of the diversity of Th cells, the relationship between scRNA-Seq clusters and the traditionally characterized Th subsets remains ambiguous.

Methods

In this study, we introduce TCR-Track, a method leveraging immune repertoire data to map phenotypically sorted Th subsets onto scRNA-Seq profiles.

Results and discussion

This approach accurately positions the Th1, Th1-17, Th17, Th22, Th2a, Th2, T follicular helper (Tfh), and regulatory T-cell (Treg) subsets, outperforming mapping based on CITE-Seq. Remarkably, the mapping is tightly focused on specific scRNA-Seq clusters, despite 4-year interval between subset sorting and the effector CD4+ scRNA-Seq experiment. These findings highlight the intrinsic program stability of Th clones circulating in peripheral blood. Repertoire overlap analysis at the scRNA-Seq level confirms that the circulating Th1, Th2, Th2a, Th17, Th22, and Treg subsets are clonally independent. However, a significant clonal overlap between the Th1 and cytotoxic CD4+ T-cell clusters suggests that cytotoxic CD4+ T cells differentiate from Th1 clones. In addition, this study resolves a longstanding ambiguity: we demonstrate that, while CCR10+ Th cells align with a specific Th22 scRNA-Seq cluster, CCR10-CCR6+CXCR3-CCR4+ cells, typically classified as Th17, represent a mixture of bona fide Th17 cells and clonally unrelated CCR10low Th22 cells. The clear distinction between the Th17 and Th22 subsets should influence the development of vaccine- and T-cell-based therapies. Furthermore, we show that severe acute SARS-CoV-2 infection induces systemic type 1 interferon (IFN) activation of naive Th cells. An increased proportion of effector IFN-induced Th cells is associated with a moderate course of the disease but remains low in critical COVID-19 cases. Using integrated scRNA-Seq, TCR-Track, and CITE-Seq data from 122 donors, we provide a comprehensive Th scRNA-Seq reference that should facilitate further investigation of Th subsets in fundamental and clinical studies.

Single-nucleus RNA sequencing reveals ARHGAP28 expression of podocytes as a biomarker in human diabetic nephropathy

Introduction

Diabetic kidney disease (DKD) represents serious diabetes-associated complications, and podocyte loss is an important histologic sign of DKD. The cellular and molecular profiles of podocytes in DKD have yet to be fully elucidated.

Methods

This study analyzed kidney-related single-nucleus RNA-seq datasets (GSE131882, GSE121862, and GSE141115) and human diabetic kidney glomeruli transcriptome profiling (GSE30122). ARHGAP28 expression was validated by western blot and immunohistochemistry.

Results

In human kidney tissues, 154 differentially expressed genes (DEGs) were identified in podocytes, which were enriched in biological processes related to nephron development and extracellular matrix-receptor interactions. Similarly, in the mouse kidney, 344 DEGs were found, clustering in pathways associated with renal development and signaling mechanisms like PI3K/Akt (phosphatidylinositol-3 kinase/protein kinase B) and PPAR (peroxisome proliferator-activated receptor). In diabetic human kidney glomeruli, 438 DEGs were identified, showing significant enrichment in pathways related to diabetic nephropathy. Venn analysis revealed 22 DEGs common across human and mouse podocytes and diabetic glomeruli, with ARHGAP28 being notably overexpressed in podocytes. The diabetic nephropathy model using db/db mice showed that ARHGAP28 expression was significantly upregulated in the kidney cortex and glomeruli. In vitro studies using a high-glucose podocyte model corroborated these findings.

Conclusions

Collectively, this study provides an insight into the function and diagnosis of DKD and indicates that ARHGAP28 in podocytes is a potential biomarker of DKD.

Single-cell omics technologies - Fundamentals on how to create single-cell looking glasses for reproductive health

Over the last decade, in line with the goals of precision medicine to offer individualized patient care, various single-cell technologies measuring gene and proteomic expression in various tissues have rapidly advanced to study health and disease at the single cell level. Precisely understanding cell composition, position within tissues, signaling pathways, and communication can reveal insights into disease mechanisms and systemic changes during development, pregnancy, and gynecologic disorders across the lifespan. Single-cell technologies dissect the complex cellular compositions of reproductive tract tissues, providing insights into mechanisms behind reproductive tract dysfunction which impact wellness and quality of life. These technologies aim to understand basic tissue and organ functions and, clinically, to develop novel diagnostics, early disease biomarkers, and cell-targeted therapies for currently suboptimally-treated disorders. Increasingly, they are applied to pregnancy and pregnancy disorders, gynecologic malignancies, and uterine and ovarian physiology and aging, which are discussed in more detail in manuscripts in this special issue of AJOG. Here, we review recent applications of single-cell technologies to the study of gynecologic disorders and systemic biological adaptations during fetal development, pregnancy, and across a woman's lifespan. We discuss sequencing- and proteomic-based single-cell methods, as well as spatial transcriptomics and high-dimensional proteomic imaging, describing each technology's mechanism, workflow, quality control, and highlighting specific benefits, drawbacks, and utility in the context of reproductive medicine. We consider analytical methods for the high-dimensional single-cell data generated, highlighting statistical constraints and recent computational techniques for downstream clinical translation. Overall, current and evolving single-cell "looking glasses", or perspectives, have the potential to transform fundamental understanding of women's health and reproductive disorders and alter the trajectory of clinical practice and patient outcomes in the future.

Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing

Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues. snRNA-seq overcomes limitations of traditional single-cell RNA-seq by using nuclei instead of cells, allowing to utilize frozen tissues and difficult-to-isolate cell types. Although various nuclei isolation methods have been developed, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for the rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissues: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods successfully captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-type-specific and cell-state-specific markers. Moreover, isolation workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation.

MOTIVATION

Single-nucleus RNA sequencing (snRNA-seq) has become an essential tool for transcriptomic analysis of complex tissues. However, the quality and efficiency of data generation depend heavily on the method used for nuclear isolation. The existing isolation techniques vary in their ability to preserve nuclear integrity, minimize ambient RNA contamination, and optimize recovery rates. Despite these differences in quality, a systematic comparison of these methods, specifically for brain tissue, is lacking. This gap poses a challenge for researchers in choosing the most suitable approach for their particular experimental requirements. To address this critical issue, our study directly compared three nuclei isolation methods and evaluated their performance in terms of yield, purity, and downstream sequencing quality. By providing a comprehensive assessment, we aim to guide researchers in selecting the most appropriate isolation protocol for their snRNA-seq experiments, ensuring optimal results and advancing the study of complex brain tissues at the single-nucleus level.

Not all controls are made equal: Definition of human kidney reference samples by single cell gene expression profiles

Identifying kidney disease mechanisms often requires comparing samples from disease states with healthy reference tissues. However, the effect of variations in sample procurement, storage and donor baseline characteristics of reference samples has thus far not been evaluated. Three distinct kidney reference sample types were evaluated for integrity and injury biomarkers and in their ability to define differentially expressed genes (DEGs) when compared to three different diabetic kidney disease (DKD) states. Unaffected parts of tumor nephrectomies (TN), pre-transplant living donor biopsies (LD), and percutaneous kidney research biopsies from healthy volunteers (HC) served as sources for reference tissue. Single cell gene expression profiles showed differences in the expression of injury or disease markers and the proportion of immune and proximal cell states. TN exhibited the highest expression of early stress response genes. A gene set associated with procurement effect in post-operative biopsies (LD and TN) was identified. An age-associated transcriptional signature was extracted from the reference data. Providing these tools to control for age and tissue procurement effects, immune-related pathways were found to be most enriched in DKD when compared to HC. Energy-related processes were enriched in DEGs from DKD versus LD. TN samples exhibited more underlying pathology than LD. The pathway analyses using the DEGs underscore the importance of accounting for appropriate confounding factors in differential expression analyses between disease and reference samples. Comparable controls are essential for appropriate molecular evaluation of pathologic tissues.

TRANSLATIONAL STATEMENT

Integrated single-cell data analysis of three reference sample types-needle biopsy from young healthy kidney tissue, pre-perfusion biopsy from transplant kidneys, and cancer-free tissue from tumor-nephrectomies-revealed distinct transcriptional profiles influenced by the biopsy procurement method and age. These differences impacted findings in diabetes-related kidney disease versus reference comparisons highlighting the need and providing the tools to account for these differences in interpreting analyses and identifying disease mechanisms.

Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas

The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

A single-cell RNA sequencing atlas of the healthy canine lung: a foundation for comparative studies

Single cell RNA sequencing (scRNA-seq) can be used to resolve the cellular and molecular heterogeneity within a tissue by identifying cell populations with an unprecedented granularity along with their transcriptional signatures. Yet, the single cell gene expression profiles of cell populations in the healthy canine lung tissue remain unexplored and such analysis could reveal novel cell populations or markers lacking in dogs and facilitate comparisons with lung diseases. Using fresh healthy lung biopsies from four dogs, we conducted droplet-based scRNA-seq on 26,278 cells. We characterized 46 transcriptionally distinct cell subpopulations across all lung tissue compartments including 23 immune, 13 mesenchymal, five epithelial and five endothelial cell subpopulations. Of note, we captured rare cells such as unconventional T cells or Schwann cells. Differential gene expression profiles identified specific markers across all cell subpopulations. Fibroblasts clusters exhibited a marked transcriptional heterogeneity, some of which might exert immune regulatory functions. Finally, the integration of canine lung cells with an annotated human lung atlas highlighted many similarities in gene expression profiles between species. This study thus provides an extensive molecular cell atlas of the healthy canine lung, expanding our knowledge of lung cell diversity in dogs, and providing the molecular foundation for investigating lung cell identities and functions in canine lung diseases. Besides, the occurrence of spontaneous lung diseases in pet dogs, with phenotypes closely resembling those in humans, may provide a relevant model for advancing research into human lung diseases.

Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor

Background

Wilms tumor (WT) is the most common childhood renal malignancy, with recurrence linked to poor prognosis. Identifying the molecular features of tumor phenotypes that drive recurrence and discovering novel targets are crucial for improving treatment strategies and enhancing patient outcomes.

Methods

Single-nuclei RNA sequencing (snRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, and mutation/copy number data were curated from public databases. The Seurat package was used to process snRNA-seq and ST data. Scissor analysis was applied to identify tumor subpopulations associated with poor relapse-free survival (RFS). Univariate Cox and LASSO analyses were utilized to reduce features. A prognostic ensemble machine learning model was developed. Immunohistochemistry was used to validate the expression of key features in tumor tissues. The CellChat and Commot package was utilized to infer cellular interactions. The PERCEPTION computational pipeline was used to predict the response of tumor cells to chemotherapy and targeted therapies.

Results

By integrating snRNA-seq and bulk RNA-seq data, we identified a subtype of Scissor+ tumor cells associated with poor RFS, predominantly derived from cap mesenchyme-like blastemal and fibroblast-like tumor subgroups. These cells displayed nephron progenitor signatures and cancer stem cell markers. A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. TGFA was identified as the most significant feature in this model and validated by immunohistochemistry. Cellular communication analysis revealed strong associations between Scissor+ tumor cells and cancer-associated fibroblasts (CAFs) through IGF, SLIT, FGF, and PDGF pathways. ST data revealed that Scissor+ tumor cells were primarily located in immune-desert niche surrounded by CAFs. Despite reduced responsiveness to conventional chemotherapy, Scissor+ tumor cells were sensitive to EGFR inhibitors, providing insights into clinical intervention strategies for WT patients at high risk of recurrence.

Conclusion

This study identified a relapse-associated tumor subtype resembling nephron progenitor cells, residing in immune-desert niches through interactions with CAFs. The proposed prognostic model could accurately identify patients at high risk of relapse, offering a promising method for clinical risk stratification. Targeting these cells with EGFR inhibitors, in combination with conventional chemotherapy, may provide a potential therapeutic strategy for WT patients.

Are we there yet? Exploring astrocyte heterogeneity one cell at a time

Astrocytes are a highly abundant cell type in the brain and spinal cord. Like neurons, astrocytes can be molecularly and functionally distinct to fulfill specialized roles. Recent technical advances in sequencing-based single cell assays have driven an explosion of omics data characterizing astrocytes in the healthy, aged, injured, and diseased central nervous system. In this review, we will discuss recent studies which have furthered our understanding of astrocyte biology and heterogeneity, as well as discuss the limitations and challenges of sequencing-based single cell and spatial genomics methods and their potential future utility.

Pig jejunal single-cell RNA landscapes revealing breed-specific immunology differentiation at various domestication stages

Background

Domestication of wild boars into local and intensive pig breeds has driven adaptive genomic changes, resulting in significant phenotypic differences in intestinal immune function. The intestine relies on diverse immune cells, but their evolutionary changes during domestication remain poorly understood at single-cell resolution.

Methods

We performed single-cell RNA sequencing (scRNA-seq) and marker gene analysis on jejunal tissues from wild boars, a Chinese local breed (Jinhua), and an intensive breed (Duroc). Then, we developed an immune cell evaluation system that includes immune scoring, gene identification, and cell communication analysis. Additionally, we mapped domestication-related clustering relationships, highlighting changes in gene expression and immune function.

Results

We generated a single-cell atlas of jejunal tissues, analyzing 26,246 cells and identifying 11 distinct cell lineages, including epithelial and plasma cells, and discovered shared and unique patterns in intestinal nutrition and immunity across breeds. Immune cell evaluation analysis confirmed the conservation and heterogeneity of immune cells, manifested by highly conserved functions of immune cell subgroups, but wild boars possess stronger immune capabilities than domesticated breeds. We also discovered four patterns of domestication-related breed-specific genes related to metabolism, immune surveillance, and cytotoxic functions. Lastly, we identified a unique population of plasma cells with distinctive antibody production in Jinhua pig population.

Conclusions

Our findings provide valuable single-cell insights into the cellular heterogeneity and immune function evolution in the jejunum during pig at various domestication stages. The single-cell atlas also serves as a resource for comparative studies and supports breeding programs aimed at enhancing immune traits in pigs.

A versatile and efficient method to isolate nuclei from low-input cryopreserved tissues for single-nuclei transcriptomics

Clinical samples are vital for understanding diseases, but their scarcity requires refined research methods. Emerging single-cell technologies offer detailed views of tissue heterogeneity but need sufficient fully characterized tissues. We developed an optimized single-nuclei RNA sequencing (snRNA-seq) protocol to extract nuclei from just 15 mg of cryopreserved human tissue. Applied to four cancer tissues (brain, bladder, lung, prostate), it profiled 1550-7468 nuclei per tissue, revealing heterogeneity comparable to public single-cell atlases. This method enhances the use and sharing of rare, cryopreserved biospecimens, supporting research where sample quantity is limited and full tissue characterization is needed.

Single-cell sequencing provides clues about the developmental genetic basis of evolutionary adaptations in syngnathid fishes

Seahorses, pipefishes, and seadragons are fishes from the family Syngnathidae that have evolved extraordinary traits including male pregnancy, elongated snouts, loss of teeth, and dermal bony armor. The developmental genetic and cellular changes that led to the evolution of these traits are largely unknown. Recent syngnathid genome assemblies revealed suggestive gene content differences and provided the opportunity for detailed genetic analyses. We created a single-cell RNA sequencing atlas of Gulf pipefish embryos to understand the developmental basis of four traits: derived head shape, toothlessness, dermal armor, and male pregnancy. We completed marker gene analyses, built genetic networks, and examined the spatial expression of select genes. We identified osteochondrogenic mesenchymal cells in the elongating face that express regulatory genes bmp4, sfrp1a, and prdm16. We found no evidence for tooth primordia cells, and we observed re-deployment of osteoblast genetic networks in developing dermal armor. Finally, we found that epidermal cells expressed nutrient processing and environmental sensing genes, potentially relevant for the brooding environment. The examined pipefish evolutionary innovations are composed of recognizable cell types, suggesting that derived features originate from changes within existing gene networks. Future work addressing syngnathid gene networks across multiple stages and species is essential for understanding how the novelties of these fish evolved.

GBMPurity: A Machine Learning Tool for Estimating Glioblastoma Tumour Purity from Bulk RNA-seq Data

BACKGROUND

Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumour purity, the proportion of malignant cells within a tumour, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumour purity are non-specific and technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM.

METHODS

We developed GBMPurity, a deep-learning model specifically designed to estimate the purity of IDH-wildtype primary GBM from bulk RNA-seq data. The model was trained using simulated pseudobulk tumours of known purity from labelled single-cell data acquired from the GBmap resource. The performance of GBMPurity was evaluated and compared to several existing tools using independent datasets.

RESULTS

GBMPurity outperformed existing tools, achieving a mean absolute error of 0.15 and a concordance correlation coefficient of 0.88 on validation datasets. We demonstrate the utility of GBMPurity through inference on bulk RNA-seq samples and observe reduced purity of the Proneural molecular subtype relative to the Classical, attributed to the increased presence of healthy brain cells.

CONCLUSIONS

GBMPurity provides a reliable and accessible tool for estimating tumour purity from bulk RNA-seq data, enhancing the interpretation of bulk RNA-seq data and offering valuable insights into GBM biology. To facilitate the use of this model by the wider research community, GBMPurity is available as a web-based tool at: https://gbmdeconvoluter.leeds.ac.uk/.

Single-cell multiomics to advance cell therapy

Single-cell RNA-sequencing (scRNAseq) was first introduced in 2009 and has evolved with many technological advancements over the last decade. Not only are there several scRNAseq platforms differing in many aspects, but there are also a large number of computational pipelines available for downstream analyses which are being developed at an exponential rate. Such computational data appear in many scientific publications in virtually every field of study; thus, investigators should be able to understand and interpret data in this rapidly evolving field. Here, we discuss key differences in scRNAseq platforms, crucial steps in scRNAseq experiments, standard downstream analyses and introduce newly developed multimodal approaches. We then discuss how single-cell omics has been applied to advance the field of cell therapy.

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, that is, benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark data sets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark data sets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark data set is available.

Personalising glioblastoma medicine: explant organoid applications, challenges and future perspectives

Glioblastoma (GBM) is a highly aggressive adult brain cancer, characterised by poor prognosis and a dismal five-year survival rate. Despite significant knowledge gains in tumour biology, meaningful advances in patient survival remain elusive. The field of neuro-oncology faces many disease obstacles, one being the paucity of faithful models to advance preclinical research and guide personalised medicine approaches. Recent technological developments have permitted the maintenance, expansion and cryopreservation of GBM explant organoid (GBO) tissue. GBOs represent a translational leap forward and are currently the state-of-the-art in 3D in vitro culture system, retaining brain cancer heterogeneity, and transiently maintaining the immune infiltrate and tumour microenvironment (TME). Here, we provide a review of existing brain cancer organoid technologies, in vivo xenograft approaches, evaluate in-detail the key advantages and limitations of this rapidly emerging technology, and consider solutions to overcome these difficulties. GBOs currently hold significant promise, with the potential to emerge as the key translational tool to synergise and enhance next-generation omics efforts and guide personalised medicine approaches for brain cancer patients into the future.

Comparative evaluation of ACetic - MEthanol high salt dissociation approach for single-cell transcriptomics of frozen human tissues

Current dissociation methods for solid tissues in scRNA-seq studies do not guarantee intact single-cell isolation, especially for sensitive and complex human endocrine tissues. Most studies rely on enzymatic dissociation of fresh samples or nuclei isolation from frozen samples. Dissociating whole intact cells from fresh-frozen samples, commonly collected by biobanks, remains a challenge. Here, we utilized the acetic-methanol dissociation approach (ACME) to capture transcriptional profiles of individual cells from fresh-frozen tissue samples. This method combines acetic acid-based dissociation and methanol-based fixation. In our study, we optimized this approach for human endocrine tissue samples for the first time. We incorporated a high-salt washing buffer instead of the standard PBS to stabilize RNA and prevent RNases reactivation during rehydration. We have designated this optimized protocol as ACME HS (ACetic acid-MEthanol High Salt). This technique aims to preserve cell morphology and RNA integrity, minimizing transcriptome changes and providing a more accurate representation of mature mRNA. We compared the ability of enzymatic, ACME HS, and nuclei isolation methods to preserve major cell types, gene expression, and standard quality parameters across 41 tissue samples. Our results demonstrated that ACME HS effectively dissociates and fixes cells, preserving cell morphology and high RNA integrity. This makes ACME HS a valuable alternative for scRNA-seq protocols involving challenging tissues where obtaining a live cell suspension is difficult or disruptive.

Optimized methods for scRNA-seq and snRNA-seq of skeletal muscle stored in nucleic acid stabilizing preservative

Single cell studies have transformed our understanding of cellular heterogeneity in disease but the need for fresh starting material can be an obstacle, especially in the context of international multicenter studies and archived tissue. We developed a protocol to obtain high-quality cells and nuclei from dissected human skeletal muscle archived in the preservative Allprotect® Tissue Reagent. After fluorescent imaging microscopy confirmed intact nuclei, we performed four protocol variations that compared sequencing metrics between cells and nuclei enriched by either filtering or flow cytometry sorting. Cells and nuclei (either sorted or filtered) produced statistically identical transcriptional profiles and recapitulated 8 cell types present in skeletal muscle. Flow cytometry sorting successfully enriched for higher-quality cells and nuclei but resulted in an overall decrease in input material. Our protocol provides an important resource for obtaining high-quality single cell genomic material from archived tissue and to streamline global collaborative efforts.

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.

Considerations for building and using integrated single-cell atlases

The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Advancements in the Application of scRNA-Seq in Breast Research: A Review

Single-cell sequencing technology provides apparent advantages in cell population heterogeneity, allowing individuals to better comprehend tissues and organs. Sequencing technology is currently moving beyond the standard transcriptome to the single-cell level, which is likely to bring new insights into the function of breast cells. In this study, we examine the primary cell types involved in breast development, as well as achievements in the study of scRNA-seq in the microenvironment, stressing the finding of novel cell subsets using single-cell approaches and analyzing the problems and solutions to scRNA-seq. Furthermore, we are excited about the field's promising future.

Single-nucleus transcriptomic analysis reveals the regulatory circuitry of myofiber XBP1 during regenerative myogenesis

Endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) is activated in skeletal muscle under multiple conditions. However, the role of the UPR in the regulation of muscle regeneration remains less understood. We demonstrate that gene expression of various markers of the UPR is induced in both myogenic and non-myogenic cells in regenerating muscle. Genetic ablation of X-box binding protein 1 (XBP1), a downstream target of the Inositol requiring enzyme 1α (IRE1α) arm of the UPR, in myofibers attenuates muscle regeneration in adult mice. Single nucleus RNA sequencing (snRNA-seq) analysis showed that deletion of XBP1 in myofibers perturbs proteolytic systems and mitochondrial function in myogenic cells. Trajectory analysis of snRNA-seq dataset showed that XBP1 regulates the abundance of satellite cells and the formation of new myofibers in regenerating muscle. In addition, ablation of XBP1 disrupts the composition of non-myogenic cells in injured muscle microenvironment. Collectively, our study suggests that myofiber XBP1 regulates muscle regeneration through both cell-autonomous and -non-autonomous mechanisms.

Single-cell transcriptomic and cell‑type‑specific regulatory networks in Polima temperature-sensitive cytoplasmic male sterility of Brassica napus L

BACKGROUND

Thermosensitive male sterility (TMS) is a heritable agronomic trait influenced by the interaction between genotype and environment. The anthers of plants are composed of various specialized cells, each of which plays different roles in plant reproduction. In rapeseed (Brassica napus L.), Polima (pol) temperature-sensitive cytoplasmic male sterility (TCMS) is widely used in two-line breeding because its fertility can be partially restored at certain temperatures. The pol-TCMS line exhibits abnormal anther development and pollen abortion at high (restrictive) temperatures (HT, 25 °C) compared to at low (permissive) temperatures (LT, 16 °C). However, the response of different anther cell types to HT and the dynamic regulation of genes under such conditions remain largely unknown.

RESULTS

We present the first single-cell transcriptomic atlas of Brassica napus early developing flower bud tissues in response to HT. We identified 8 cell types and 17 transcriptionally distinct cell clusters via known marker genes under LT and HT treatment conditions. Under HT conditions, changes in the gene expression patterns of different cell clusters were observed, with the number of down-regulated genes in various cell types exceeding that of up-regulated genes. Pseudotime trajectory analysis revealed that HT strongly affected the development of early stamen/anther tissue cells. In combination with the snRNA-seq, WGCNA, and bulk RNA-seq results, we found that many transcription factors play crucial roles in the response to HT, especially heat response family genes.

CONCLUSIONS

Our study revealed the transcriptional regulatory network of floral bud tissue in the pol-TCMS line under HT/LT conditions and increased our understanding of high-temperature-induced anther developmental abnormalities, which may help researchers utilize TCMS in the two-line breeding of Brassica plants.

SLAM/SAP signaling regulates discrete γδ T cell developmental checkpoints and shapes the innate-like γδ TCR repertoire

During thymic development, most γδ T cells acquire innate-like characteristics that are critical for their function in tumor surveillance, infectious disease, and tissue repair. The mechanisms, however, that regulate γδ T cell developmental programming remain unclear. Recently, we demonstrated that the SLAM/SAP signaling pathway regulates the development and function of multiple innate-like γδ T cell subsets. Here, we used a single-cell proteogenomics approach to identify SAP-dependent developmental checkpoints and to define the SAP-dependent γδ TCR repertoire in mice. SAP deficiency resulted in both a significant loss of an immature Gzma+Blk+Etv5+Tox2+ γδT17 precursor population and a significant increase in Cd4+Cd8+Rorc+Ptcra+Rag1+ thymic γδ T cells. SAP-dependent diversion of embryonic day 17 thymic γδ T cell clonotypes into the αβ T cell developmental pathway was associated with a decreased frequency of mature clonotypes in neonatal thymus, and an altered γδ TCR repertoire in the periphery. Finally, we identify TRGV4/TRAV13-4(DV7)-expressing T cells as a novel, SAP-dependent Vγ4 γδT1 subset. Together, the data support a model in which SAP-dependent γδ/αβ T cell lineage commitment regulates γδ T cell developmental programming and shapes the γδ TCR repertoire.

Single-Cell Transcriptomics Sheds Light on Tumor Evolution: Perspectives from City of Hope's Clinical Trial Teams

Tumor heterogeneity is a significant factor influencing cancer treatment effectiveness and can arise from genetic, epigenetic, and phenotypic variations among cancer cells. Understanding how tumor heterogeneity impacts tumor evolution and therapy response can lead to more effective treatments and improved patient outcomes. Traditional bulk genomic approaches fail to provide insights into cellular-level events, whereas single-cell RNA sequencing (scRNA-seq) offers transcriptomic analysis at the individual cell level, advancing our understanding of tumor growth, progression, and drug response. However, implementing single-cell approaches in clinical trials involves challenges, such as obtaining high-quality cells, technical variability, and the need for complex computational analysis. Effective implementation of single-cell genomics in clinical trials requires a collaborative "Team Medicine" approach, leveraging shared resources, expertise, and workflows. Here, we describe key technical considerations in implementing the collection of research biopsies and lessons learned from integrating scRNA-seq into City of Hope's clinical trial design, highlighting collaborative efforts between computational and clinical teams across breast, brain, and ovarian cancer studies to understand the composition, phenotypic state, and underlying resistance mechanisms within the tumor microenvironment.

Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges, and future perspectives on transcriptomic profiling

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic approach with the power to identify rare cells, discover new cellular subclusters, and describe novel genes. scRNA-seq can simultaneously reveal dynamic shifts in cellular phenotypes and heterogeneities in cellular subtypes. Since the publication of the first protocol on scRNA-seq in 2009, this evolving technology has continued to improve, through the use of cell-specific barcodes, adoption of droplet-based systems, and development of advanced computational methods. Despite induction of the cellular stress response during the tissue dissociation process, scRNA-seq remains a popular technology, and commercially available scRNA-seq methods have been applied to the brain. Recent advances in spatial transcriptomics now allow the researcher to capture the positional context of transcriptional activity, strengthening our knowledge of cellular organization and cell-cell interactions in spatially intact tissues. A combination of spatial transcriptomic data with proteomic, metabolomic, or chromatin accessibility data is a promising direction for future research. Herein, we provide an overview of the workflow, data analyses methods, and pros and cons of scRNA-seq technology. We also summarize the latest achievements of scRNA-seq in stroke and acute traumatic brain injury, and describe future applications of scRNA-seq and spatial transcriptomics.

An organotypic atlas of human vascular cells

The human vascular system, comprising endothelial cells (ECs) and mural cells, covers a vast surface area in the body, providing a critical interface between blood and tissue environments. Functional differences exist across specific vascular beds, but their molecular determinants across tissues remain largely unknown. In this study, we integrated single-cell transcriptomics data from 19 human organs and tissues and defined 42 vascular cell states from approximately 67,000 cells (62 donors), including angiotypic transitional signatures along the arterial endothelial axis from large to small caliber vessels. We also characterized organotypic populations, including splenic littoral and blood-brain barrier ECs, thus clarifying the molecular profiles of these important cell states. Interrogating endothelial-mural cell molecular crosstalk revealed angiotypic and organotypic communication pathways related to Notch, Wnt, retinoic acid, prostaglandin and cell adhesion signaling. Transcription factor network analysis revealed differential regulation of downstream target genes in tissue-specific modules, such as those of FOXF1 across multiple lung vascular subpopulations. Additionally, we make mechanistic inferences of vascular drug targets within different vascular beds. This open-access resource enhances our understanding of angiodiversity and organotypic molecular signatures in human vascular cells, and has therapeutic implications for vascular diseases across tissues.

Quality control for single-cell analysis of high-plex tissue profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.

A Roadmap for Selecting and Utilizing Optimal Features in scRNA Sequencing Data Analysis for Stem Cell Research: A Comprehensive Review

Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.

High content of nuclei-free low-quality cells in reference single-cell atlases: a call for more stringent quality control using nuclear fraction

The advent of droplet-based single-cell RNA-sequencing (scRNA-seq) has dramatically increased data throughput, enabling the release of a diverse array of tissue cell atlases to the public. However, we will show that prominent initiatives such as the Human Cell Atlas [1], the Tabula Sapiens [2] and the Tabula Muris [3] contain a significant amount of contamination products (frequently affecting the whole organ) in their data portals due to suboptimal quality filtering. Our work addresses a critical gap by advocating for more stringent quality filtering, highlighting the imperative for a shift from existing standards, which currently lean towards greater permissiveness. We will show the importance of incorporating cell intronic fraction in quality control -or MALAT1 expression otherwise- showcasing its informative nature and potential to elevate cell atlas data reliability. In summary, here, we unveil the hidden intronic landscape of every tissue and highlight the importance of more rigorous single-cell RNA-sequencing quality assessment in cell atlases to enhance their applicability in diverse downstream analyses.

Integrated single-cell analysis reveals distinct epigenetic-regulated cancer cell states and a heterogeneity-guided core signature in tamoxifen-resistant breast cancer

BACKGROUND

Inter- and intra-tumor heterogeneity is considered a significant factor contributing to the development of endocrine resistance in breast cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) allow us to explore inter- and intra-tumor heterogeneity at single-cell resolution. However, such integrated single-cell analysis has not yet been demonstrated to characterize the transcriptome and chromatin accessibility in breast cancer endocrine resistance.

METHODS

In this study, we conducted an integrated analysis combining scRNA-seq and scATAC-seq on more than 80,000 breast tissue cells from two normal tissues (NTs), three primary tumors (PTs), and three tamoxifen-treated recurrent tumors (RTs). A variety of cell types among breast tumor tissues were identified, PT- and RT-specific cancer cell states (CSs) were defined, and a heterogeneity-guided core signature (HCS) was derived through such integrated analysis. Functional experiments were performed to validate the oncogenic role of BMP7, a key gene within the core signature.

RESULTS

We observed a striking level of cell-to-cell heterogeneity among six tumor tissues and delineated the primary to recurrent tumor progression, underscoring the significance of these single-cell level tumor cell clusters classified from scRNA-seq data. We defined nine CSs, including five PT-specific, three RT-specific, and one PT-RT-shared CSs, and identified distinct open chromatin regions of CSs, as well as a HCS of 137 genes. In addition, we predicted specific transcription factors (TFs) associated with the core signature and novel biological/metabolism pathways that mediate the communications between CSs and the tumor microenvironment (TME). We finally demonstrated that BMP7 plays an oncogenic role in tamoxifen-resistant breast cancer cells through modulating MAPK signaling pathways.

CONCLUSIONS

Our integrated single-cell analysis provides a comprehensive understanding of the tumor heterogeneity in tamoxifen resistance. We envision this integrated single-cell epigenomic and transcriptomic measure will become a powerful approach to unravel how epigenetic factors and the tumor microenvironment govern the development of tumor heterogeneity and to uncover potential therapeutic targets that circumvent heterogeneity-related failures.

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities, such as bulk ATAC-seq data.

Characterizing Fibroblast Heterogeneity in Diabetic Wounds Through Single-Cell RNA-Sequencing

Diabetes mellitus is an increasingly prevalent chronic metabolic disorder characterized by physiologic hyperglycemia that, when left uncontrolled, can lead to significant complications in multiple organs. Diabetic wounds are common in the general population, yet the underlying mechanism of impaired healing in such wounds remains unclear. Single-cell RNA-sequencing (scRNAseq) has recently emerged as a tool to study the gene expression of heterogeneous cell populations in skin wounds. Herein, we review the history of scRNAseq and its application to the study of diabetic wound healing, focusing on how innovations in single-cell sequencing have transformed strategies for fibroblast analysis. We summarize recent research on the role of fibroblasts in diabetic wound healing and describe the functional and cellular heterogeneity of skin fibroblasts. Moreover, we highlight future opportunities in diabetic wound fibroblast research, with a focus on characterizing distinct fibroblast subpopulations and their lineages. Leveraging single-cell technologies to explore fibroblast heterogeneity and the complex biology of diabetic wounds may reveal new therapeutic targets for improving wound healing and ultimately alleviate the clinical burden of chronic wounds.

Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases

Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.

Concepts and new developments in droplet-based single cell multi-omics

Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.

Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages

Breast cancer (BC) is defined by distinct molecular subtypes with different cells of origin. The transcriptional networks that characterize the subtype-specific tumor-normal lineages are not established. In this work, we applied bulk, single-cell and single-nucleus multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 patients with BC to show characteristic links in gene expression and chromatin accessibility between BC subtypes and their putative cells of origin. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal BC and luminal mature cells and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like (SOX6 and KCNQ3) and luminal A/B (FAM155A and LRP1B) lineages. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like BC, suggesting an altered means of immune dysfunction. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single-cell level is a powerful tool for investigating cancer lineage and highlight transcriptional networks that define basal and luminal BC lineages.

Optimized protocol for the multiomics processing of cryopreserved human kidney tissue

Biobanking of tissue from clinically obtained kidney biopsies for later analysis with multiomic approaches, such as single-cell technologies, proteomics, metabolomics, and the different types of imaging, is an inevitable step to overcome the need of disease model systems and toward translational medicine. Hence, collection protocols that ensure integration into daily clinical routines by the usage of preservation media that do not require liquid nitrogen but instantly preserve kidney tissue for both clinical and scientific analyses are necessary. Thus, we modified a robust single-nucleus dissociation protocol for kidney tissue stored snap-frozen or in the preservation media RNAlater and CellCover. Using at first porcine kidney tissue as a surrogate for human kidney tissue, we conducted single-nucleus RNA sequencing with the widely recognized Chromium 10X Genomics platform. The resulting datasets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques such as proteomics, metabolomics, and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines, the preservation medium RNAlater facilitates the collection of highly preserved human kidney biopsies and enables further analysis with cutting-edge techniques like single-nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap-frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening up new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.NEW & NOTEWORTHY In this study, we addressed challenges in integrating clinically obtained kidney biopsies into everyday clinical routines. Using porcine kidneys, we evaluated preservation media (RNAlater and CellCover) versus snap freezing for multi-omics processing. Our analyses highlighted RNAlater's suitability for single-nucleus RNA sequencing, proteome analysis and histopathological evaluation. Only metabolomics are currently restricted to snap-frozen biopsies. Our research established a cryopreservation protocol that facilitates tissue biobanking for advancing precision medicine in nephrology.

High-parametric protein maps reveal the spatial organization in early-developing human lung

The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lung and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

Multiomics profiling of mouse polycystic kidney disease progression at a single-cell resolution

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to kidney failure. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single-nucleus multimodal atlas of an orthologous mouse PKD model at early, mid, and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalog differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single-nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.

NBAtlas: A harmonized single-cell transcriptomic reference atlas of human neuroblastoma tumors

Neuroblastoma, a rare embryonic tumor arising from neural crest development, is responsible for 15% of pediatric cancer-related deaths. Recently, several single-cell transcriptome studies were performed on neuroblastoma patient samples to investigate the cell of origin and tumor heterogeneity. However, these individual studies involved a small number of tumors and cells, limiting the conclusions that could be drawn. To overcome this limitation, we integrated seven single-cell or single-nucleus datasets into a harmonized cell atlas covering 362,991 cells across 61 patients. We use this atlas to decipher the transcriptional landscape of neuroblastoma at single-cell resolution, revealing associations between transcriptomic profiles and clinical outcomes within the tumor compartment. In addition, we characterize the complex immune-cell landscape and uncover considerable heterogeneity among tumor-associated macrophages. Finally, we showcase the utility of our atlas as a resource by expanding it with additional data and using it as a reference for data-driven cell-type annotation.

A spatiotemporal molecular atlas of mouse spinal cord injury identifies a distinct astrocyte subpopulation and therapeutic potential of IGFBP2

Spinal cord injury (SCI) triggers a cascade of intricate molecular and cellular changes that determine the outcome. In this study, we resolve the spatiotemporal organization of the injured mouse spinal cord and quantitatively assess in situ cell-cell communication following SCI. By analyzing existing single-cell RNA sequencing datasets alongside our spatial data, we delineate a subpopulation of Igfbp2-expressing astrocytes that migrate from the white matter (WM) to gray matter (GM) and become reactive upon SCI, termed Astro-GMii. Further, Igfbp2 upregulation promotes astrocyte migration, proliferation, and reactivity, and the secreted IGFBP2 protein fosters neurite outgrowth. Finally, we show that IGFBP2 significantly reduces neuronal loss and remarkably improves the functional recovery in a mouse model of SCI in vivo. Together, this study not only provides a comprehensive molecular atlas of SCI but also exemplifies how this rich resource can be applied to endow cells and genes with functional insight and therapeutic potential.

Single-Nuclei Transcriptome Profiling Reveals Intra-Tumoral Heterogeneity and Characterizes Tumor Microenvironment Architecture in a Murine Melanoma Model

Malignant melanoma is an aggressive cancer, with a high risk of metastasis and mortality rates, characterized by cancer cell heterogeneity and complex tumor microenvironment (TME). Single cell biology is an ideal and powerful tool to address these features at a molecular level. However, this approach requires enzymatic cell dissociation that can influence cellular coverage. By contrast, single nucleus RNA sequencing (snRNA-seq) has substantial advantages including compatibility with frozen samples and the elimination of a dissociation-induced, transcriptional stress response. To better profile and understand the functional diversity of different cellular components in melanoma progression, we performed snRNA-seq of 16,839 nuclei obtained from tumor samples along the growth of murine syngeneic melanoma model carrying a BRAFV600E mutation and collected 9 days or 23 days after subcutaneous cell injection. We defined 11 different subtypes of functional cell clusters among malignant cells and 5 different subsets of myeloid cells that display distinct global transcriptional program and different enrichment in early or advanced stage of tumor growth, confirming that this approach was useful to accurately identify intratumor heterogeneity and dynamics during tumor evolution. The current study offers a deep insight into the biology of melanoma highlighting TME reprogramming through tumor initiation and progression, underlying further discovery of new TME biomarkers which may be potentially druggable.

Mouse liver sinusoidal endothelial cell responses to the glucocorticoid receptor agonist dexamethasone

Liver sinusoidal endothelial cells (LSECs) which make up the fenestrated wall of the hepatic sinusoids, are active scavenger cells involved in blood waste clearance and liver immune functions. Dexamethasone is a synthetic glucocorticoid commonly used in the clinic and as cell culture supplement. However, the response is dependent on tissue, cell type, and cell state. The aim of this study was to investigate the effect of dexamethasone on primary mouse LSECs (C57BL/6J); their viability (live-dead, LDH release, caspase 3/7 assays), morphology (scanning electron microscopy), release of inflammatory markers (ELISA), and scavenging functions (endocytosis assays), and associated biological processes and pathways. We have characterized and catalogued the proteome of LSECs cultured for 1, 10, or 48 h to elucidate time-dependent and dexamethasone-specific cell responses. More than 6,000 protein IDs were quantified using tandem mass tag technology and advanced mass spectrometry (synchronous precursor selection multi-notch MS3). Enrichment analysis showed a culture-induced upregulation of stress and inflammatory markers, and a significant shift in cell metabolism already at 10 h, with enhancement of glycolysis and concomitant repression of oxidative phosphorylation. At 48 h, changes in metabolic pathways were more pronounced with dexamethasone compared to time-matched controls. Dexamethasone repressed the activation of inflammatory pathways (IFN-gamma response, TNF-alpha signaling via NF-kB, Cell adhesion molecules), and culture-induced release of interleukin-6, VCAM-1, and ICAM-1, and improved cell viability partly through inhibition of apoptosis. The mouse LSECs did not proliferate in culture. Dexamethasone treated cells showed upregulation of xanthine dehydrogenase/oxidase (Xdh), and the transcription regulator Foxo1. The drug further delayed but did not block the culture-induced loss of LSEC fenestration. The LSEC capacity for endocytosis was significantly reduced at 48 h, independent of dexamethasone, which correlated with diminished expression of several scavenger receptors and C-type lectins and altered expression of proteins in the endocytic machinery. The glucocorticoid receptor (NR3C1) was suppressed by dexamethasone at 48 h, suggesting limited effect of the drug in prolonged LSEC culture. Conclusion: The study presents a detailed overview of biological processes and pathways affected by dexamethasone in mouse LSECs in vitro.

Single-cell profiling of cellular changes in the somatic peripheral nerves following nerve injury

Injury to the peripheral nervous system disconnects targets to the central nervous system, disrupts signal transmission, and results in functional disability. Although surgical and therapeutic treatments improve nerve regeneration, it is generally hard to achieve fully functional recovery after severe peripheral nerve injury. A better understanding of pathological changes after peripheral nerve injury helps the development of promising treatments for nerve regeneration. Single-cell analyses of the peripheral nervous system under physiological and injury conditions define the diversity of cells in peripheral nerves and reveal cell-specific injury responses. Herein, we review recent findings on the single-cell transcriptome status in the dorsal root ganglia and peripheral nerves following peripheral nerve injury, identify the cell heterogeneity of peripheral nerves, and delineate changes in injured peripheral nerves, especially molecular changes in neurons, glial cells, and immune cells. Cell-cell interactions in peripheral nerves are also characterized based on ligand-receptor pairs from coordinated gene expressions. The understanding of cellular changes following peripheral nerve injury at a single-cell resolution offers a comprehensive and insightful view for the peripheral nerve repair process, provides an important basis for the exploration of the key regulators of neuronal growth and microenvironment reconstruction, and benefits the development of novel therapeutic drugs for the treatment of peripheral nerve injury.

Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries

The advent of single-cell resolution sequencing and spatial transcriptomics has enabled the delivery of cellular and molecular atlases of tissues and organs, providing new insights into tissue health and disease. However, if the full potential of these technologies is to be equitably realised, ancestrally inclusivity is paramount. Such a goal requires greater inclusion of both researchers and donors in low- and middle-income countries (LMICs). In this perspective, we describe the current landscape of ancestral inclusivity in genomic and single-cell transcriptomic studies. We discuss the collaborative efforts needed to scale the barriers to establishing, expanding, and adopting single-cell sequencing research in LMICs and to enable globally impactful outcomes of these technologies.