Single-cell Transcriptomic Studies Research Articles

Reprogramming macrophages to treat liver diseases

Cutting-edge research has expanded our understanding of the macrophage activation programs in liver diseases making this immune cell type a therapeutic target. Clinical data on macrophage infiltration and polarization states have been used to help predict mortality or poor prognosis in patients with liver cirrhosis and/or hepatocellular carcinoma. The latest single-cell and spatial transcriptomics studies have dissected unforeseen aspects depicting the immense heterogeneity of macrophages and their multifaceted role on both promoting and resolving hepatic inflammation, injury, and fibrosis. Hepatic macrophages (resident tissue Kupffer cells and monocyte-derived macrophages) display such plasticity and phenotypic diversity that macrophages with antagonistic functions may coexist in adjacent regions of the liver. In this scenario, the analysis of macrophage-derived inflammatory and anti-inflammatory circulating soluble markers in patients with liver disease only offers a partial picture of the full complexity of the hepatic macrophage subsets. The reprogramming of macrophages involves understanding the multiple regulatory mechanisms and diverse populations of hepatic macrophages and the design of macrophage-targeted therapeutic interventions to restore hepatic homeostasis. Here we review the potential targets to modulate macrophage behavior in liver diseases and nanoscale therapeutics that aim to target and treat macrophages. We will summarize current knowledge on the diverse macrophage programs activated in chronic liver inflammation, cirrhosis and hepatocellular carcinoma that may be of therapeutic interest for precision medicine.

CAFomics: convergence to translation for precision stroma approaches.

A noticeable characteristic of pancreatic ductal adenocarcinoma (PDAC) tumors is a dense tumor microenvironment with abundant and dense, desmoplastic stroma woven tightly with both cellular and matrix components. The high stromal density is associated with higher intratumor pressures which, until the last decade, was largely assumed to be tumor protective, confirmed by early studies demonstrating that altering the stroma was effective in genetically engineered models of PDAC. However, clinical trials using these approaches have been disappointing. There is increasing recognition that stroma heterogeneity is much greater than initially thought with an explosion of investigation into cancer-associated fibroblast (CAF) subpopulations led by experimental and single-cell transcriptomic studies. This review summarizes and attempts to harmonize the current transcriptomic data of CAF subpopulations. Understanding the heterogeneity of CAFs, the matrix, and other tumor microenvironment features will be critical to developing effective therapeutic approaches. Identifying model systems that best recapitulate the clinical behavior and treatment response of human PDAC will be important. Examining subpopulations as defined by clinical outcome will remain a critical step in defining clinically impactful CAF subtypes in larger clinical cohorts. The future of precision oncology in PDAC will depend on the integration of precision tumor epithelial and precision stroma approaches.

Integrative epidemiology and immuno-transcriptomics uncover a risk and potential mechanism for cutaneous lymphoma unmasking or progression with dupilumab therapy

There have been multiple reports of the anti-IL4Rα agent, dupilumab, as associated with the onset and/or progression of cutaneous T-cell lymphoma (CTCL). We sought to evaluate safety signals associated with dupilumab, with a focus on CTCL, and to evaluate possible underlying mechanism(s) for the potential association. First, we used the FDA pharmacovigilance database, FAERS, to evaluate if dupilumab was associated with CTCL including both positive controls (conjunctivitis, eosinophilia, and arthralgia) and exposure controls (other medications with similar indications including JAK-inhibitors and the anti-IL13, tralokinumab,) to reduce and evaluate confounding bias. Thereafter, we used publicly available bulk and single cell-RNA seq datasets to probe possible underlying mechanisms through which dupilumab might be associated with CTCL. Between January 2019 and Q2 of 2023, there were 181,575 unique reports of dupilumab related adverse events (AE) in FAERS with 606 of these being for a neoplasm. Dupilumab had 30.0 times the proportional reporting ratio (PRR) (95% CI: 25.0 - 35.9) for CTCL compared to all other medications in FAERS. The risk was highest in males aged 45-65. The PRR for conjunctivitis, eosinophilia, and arthralgia, known side effects of dupilumab, were 35.6 (34.4 - 36.8), 2.15 (2.00 - 2.31), and 2.14 (2.07 - 2.18) respectively. Using the log-count normalized PRR (AE score) to account for PRR inflation when reports were small, the top safety signals included conjunctivitis (AEscore 8.3) and CTCL (AEscore 4.9). Bulk RNA sequencing data showed changes in IL4RA and IL13RA1 expression in CTCL and in epidermal layers of atopic dermatitis (AD) biopsies. Single-cell transcriptomic studies revealed that this change was similar in AD and CTCL, and that keratinocytes seemed to be the most divergent cell type with regards to IL4R and IL13RA1. An effect on keratinocyte specific gene expression was also independently observed in available bulk RNA sequencing data. These data suggest that dupilumab might be causing an unmasking or progression of CTCL via the same mechanism through which it improves atopic dermatitis: IL13 receptor blockade, which leads to increased IL13 in the local milieu, driving CTCL stimulation and progression. However, these associations need further evaluation given the inherent limitations of the FAERS database and our non-experimental approach.

Collapsible tree: interactive web app to present collapsible hierarchies.

A crucial component of intuitive data visualization is presenting a hierarchical tree structure with interactive functions. For example, single-cell transcriptomics studies may generate gene expression values with developmental trajectories or cell lineage structures. Two common visualization methods, t-SNE and UMAP, require two separate figures to depict the distribution of cell types and gene expression data, with low-dimension projections that may not capture the hierarchical structures among cells. Here, we present a JavaScript framework and an interactive web app named Collapsible Tree, which presents values jointly with interactive, expandable, and collapsible lineage structures. For example, the Collapsible Tree presents cellular states and gene expression from single-cell transcriptomics within a single hierarchical plot, enabling comparisons of gene expression across lineages and subtle patterns between sub-lineages. Our framework can facilitate the exploration of complicated value patterns that are not evident in UMAP or t-SNE plots. The Collapsible Tree web interface is available at https://collapsibletree.data2in.net. The JavaScript library source code is available at https://github.com/data2intelligence/collapsible_tree.

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease.

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

Targeting GProtein-Coupled Receptors in Immuno-Oncological Therapies.

The advent of cancer immunotherapy based on PD-1 and CTLA-4 immune checkpoint blockade (ICB) has revolutionized cancer treatment. However, many cancers do not respond to ICB, highlighting the urgent need for additional approaches to achieve durable cancer remission. The large family of G protein-coupled receptors (GPCRs) is the target of more than 30% of all approved drugs, but GPCRs have been underexploited in cancer immunotherapy. In this review, we discuss the central role of GPCRs in immune cell migration and function and describe how single-cell transcriptomic studies are illuminating the complexity of the human tumor immune GPCRome. These receptors include multiple GPCRs expressed in CD8 Tcells that are activated by inflammatory mediators, protons, neurotransmitters, and metabolites that accumulate in the tumor microenvironment, thereby promoting Tcell dysfunction. We also discuss new opportunities to target GPCRs as a multimodal approach to enhance the response to ICB for a myriad of human malignancies.

Identification of hypoxic macrophages in glioblastoma: Unveiling therapeutic insights from tumour microenvironment analysis.

Tumor-associatedmacrophages (TAMs) exhibit remarkable heterogeneity in glioblastoma. Spatially resolved single-cell transcriptomic studies identified a monocyte-derived TAM subset localized in the peri-necrotic niche, driven by hypoxic cues to acquire ahypoxia response signature. These hypoxia-TAMs destabilize endothelial adherens junctions through adrenomedullin paracrine signaling, promoting the formation of hyperpermeable neovasculature that impedes drug delivery. Blocking adrenomedullin produced by hypoxia-TAMs restores vascular integrity, increases drug deliveryinto tumors, and provides combinatorial therapeutic benefits. Here we discuss the heterogeneity of TAMs regarding functional states and locations in glioblastomas, and propose future directions for studying the temporospatial dynamics of multifaceted TAM. HIGHLIGHTS: Single-cell omics reveal a functionally and spatially distinct hypoxia-TAM subset in glioblastoma. Adrenomedullin secreted by hypoxia-TAM destabilizes tumor vasculature and its blockade enhances vessel integrity and drug delivery.

Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility.

Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.

A single-cell transcriptomic study of heterogeneity in human embryonic tanycytes

Disruptions in energy homeostasis can lead to diseases like obesity and diabetes, affecting millions of people each year. Tanycytes, the adult stem cells in the hypothalamus, play crucial roles in assisting hypothalamic neurons in maintaining energy balance. Although tanycytes have been extensively studied in rodents, our understanding of human tanycytes remains limited. In this study, we utilized single-cell transcriptomics data to explore the heterogeneity of human embryonic tanycytes, investigate their gene regulatory networks, analyze their intercellular communication, and examine their developmental trajectory. Our analysis revealed the presence of two clusters of β tanycytes and three clusters of α tanycytes in our dataset. Surprisingly, human embryonic tanycytes displayed significant similarities to mouse tanycytes in terms of marker gene expression and transcription factor activities. Trajectory analysis indicated that α tanycytes were the first to be generated, giving rise to β tanycytes in a dorsal–ventral direction along the third ventricle. Furthermore, our CellChat analyses demonstrated that tanycytes generated earlier along the developmental lineages exhibited increased intercellular communication compared to those generated later. In summary, we have thoroughly characterized the heterogeneity of human embryonic tanycytes from various angles. We are confident that our findings will serve as a foundation for future research on human tanycytes.

Organ Evolution: Emergence of Multicellular Function.

Instances of multicellularity across the tree of life have fostered the evolution of complex organs composed of distinct cell types that cooperate, producing emergent biological functions. How organs originate is a fundamental evolutionary problem that has eluded deep mechanistic and conceptual understanding. Here I propose a cell- to organ-level transitions framework, whereby cooperative division of labor originates and becomes entrenched between cell types through a process of functional niche creation, cell type subfunctionalization, and irreversible ratcheting of cell interdependencies. Comprehending this transition hinges on explaining how these processes unfold molecularly in evolving populations. Recent single-cell transcriptomic studies and analyses of terminal fate specification indicate that cellular functions are conferred by modular gene expression programs. These discrete components of functional variation may be deployed or combined within cells to introduce new properties into multicellular niches, or partitioned across cells to establish division of labor. Tracing gene expression program evolution at the level of single cells in populations may reveal transitions toward organ complexity.

Single-cell transcriptomic studies unveil potential nodes of the notochord gene regulatory network.

Transcription factors (TFs) are DNA-binding proteins able to modulate the timing, location and levels of gene expression by binding to regulatory DNA regions. Therefore, the repertoire of notochord TFs present in the genome of a multicellular organism and the expression of variable constellations of TFs in different cellular cohorts determine the distinctive characteristics of developing tissues and organs. The information on tissue-specific assortments of TFs, their cross-regulatory interactions, and the genes/regulatory regions targeted by each TF is summarized in gene regulatory networks (GRNs), which provide genetic blueprints for the specification, development and differentiation of multicellular structures. In this study, we review recent transcriptomic studies focused on the complement of TFs expressed in the notochord, a distinctive feature of all chordates. We analyzed notochord-specific datasets available from organisms representative of the three chordate subphyla, and highlighted lineage-specific variations in the suite of TFs expressed in their notochord. We framed the resulting findings within a provisional evolutionary scenario, which allows the formulation of hypotheses on the genetic/genomic changes that sculpted the structure and function of this structure on an evolutionary scale.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. https://github.com/jianzou75/MICA.

Spatial Single-cell Analysis Decodes Cortical Layer and Area Specification.

The human cerebral cortex, pivotal for advanced cognitive functions, is composed of six distinct layers and dozens of functionally specialized areas1,2. The layers and areas are distinguished both molecularly, by diverse neuronal and glial cell subtypes, and structurally, through intricate spatial organization3,4. While single-cell transcriptomics studies have advanced molecular characterization of human cortical development, a critical gap exists due to the loss of spatial context during cell dissociation5,6,7,8. Here, we utilized multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based cell segmentation, to examine the molecular, cellular, and cytoarchitectural development of human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing 16 million single cells, spans eight cortical areas across four time points in the second and third trimesters. We uncovered an early establishment of the six-layer structure, identifiable in the laminar distribution of excitatory neuronal subtypes by mid-gestation, long before the emergence of cytoarchitectural layers. Notably, while anterior-posterior gradients of neuronal subtypes were generally observed in most cortical areas, a striking exception was the sharp molecular border between primary (V1) and secondary visual cortices (V2) at gestational week 20. Here we discovered an abrupt binary shift in neuronal subtype specification at the earliest stages, challenging the notion that continuous morphogen gradients dictate mid-gestation cortical arealization6,10. Moreover, integrating single-nuclei RNA-sequencing and in situ whole transcriptomics revealed an early upregulation of synaptogenesis in V1-specific Layer 4 neurons, suggesting a role of synaptogenesis in this discrete border formation. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This work not only provides a valuable resource for the field, but also establishes a spatially resolved single-cell analysis paradigm that paves the way for a comprehensive developmental atlas of the human brain.

Boolean Network Models of Human Preimplantation Development.

Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms

IntroductionThe rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules. ObjectivesBoth ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated. MethodsTo address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells. ResultsUsing NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies. ConclusionBeyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.

New Insight into Intestinal Mast Cells Revealed by Single-Cell RNA Sequencing.

Mast cells (MCs) are tissue-resident immune cells distributed in all tissues and strategically located close to blood and lymphatic vessels and nerves. Thanks to the expression of a wide array of receptors, MCs act as tissue sentinels, able to detect the presence of bacteria and parasites and to respond to different environmental stimuli. MCs originate from bone marrow (BM) progenitors that enter the circulation and mature in peripheral organs under the influence of microenvironment factors, thus differentiating into heterogeneous tissue-specific subsets. Even though MC activation has been traditionally linked to IgE-mediated allergic reactions, a role for these cells in other pathological conditions including tumor progression has recently emerged. However, several aspects of MC biology remain to be clarified. The advent of single-cell RNA sequencing platforms has provided the opportunity to understand MCs' origin and differentiation as well as their phenotype and functions within different tissues, including the gut. This review recapitulates how single-cell transcriptomic studies provided insight into MC development as well as into the functional role of intestinal MC subsets in health and disease.

Carboxypeptidase M modulates BMSCs osteogenesis-adipogenesis via the MAPK/ERK pathway: Anintegrated single-cell and bulk transcriptomic study.

The pathogenesis of osteoporosis (OP) is closely associated with the disrupted balance between osteogenesis and adipogenesis in bone marrow-derived mesenchymal stem cells (BMSCs). We analyzed published single-cell RNA sequencing (scRNA-seq) data to dissect the transcriptomic profiles of bone marrow-derived cells in OP, reviewing 56 377 cells across eight scRNA-seq datasets from femoral heads (osteoporosis or osteopenia n = 5, osteoarthritis n = 3). Seventeen genes, including carboxypeptidase M (CPM), were identified as key osteogenesis-adipogenesis regulators through comprehensive gene set enrichment, differential expression, regulon activity, and pseudotime analyses. Invitro, CPM knockdown reduced osteogenesis and promoted adipogenesis in BMSCs, while adenovirus-mediated CPM overexpression had the reverse effects. Invivo, intraosseous injection of CPM-overexpressing BMSCs mitigated bone loss in ovariectomized mice. Integrated scRNA-seq and bulk RNA sequencing analyses provided insight into the MAPK/ERK pathway's role in the CPM-mediated regulation of BMSC osteogenesis and adipogenesis; specifically, CPM overexpression enhanced MAPK/ERK signaling and osteogenesis. In contrast, the ERK1/2 inhibitor binimetinib negated the effects of CPM overexpression. Overall, our findings identify CPM as a pivotal regulator of BMSC differentiation, which provides new clues for the mechanistic study of OP.

Heterogeneous characterization of neutrophilic cells in head and neck cancers.

Neutrophilic cells are among the most abundant immune populations within the head and neck tumor microenvironment (TME) and harbor multiple mechanisms of immunosuppression. Despite these important features, neutrophilic cells may be underrepresented in contemporary studies that aim to comprehensively characterize the immune landscape of the TME due to discrepancies in tissue processing and analysis techniques. Here, we review the role of pathologically activated neutrophilic cells within the TME and pitfalls of various approaches used to study their frequency and function in clinical samples. The literature was identified by searching PubMed for "immune landscape" and "tumor immune microenvironment" in combination with keywords describing solid tumor malignancies. Key publications that assessed the immune composition of solid tumors derived from human specimens were included. The tumor and blood processing methodologies in each study were reviewed in depth and correlated with the reported abundance of neutrophilic cells. Neutrophilic cells do not survive cryopreservation, and many studies fail to identify and study neutrophilic cell populations due to cryopreservation of clinical samples for practical reasons. Additional single-cell transcriptomic studies filter out neutrophilic cells due to low transcriptional counts. This report can help readers critically interpret studies aiming to comprehensively study the immune TME that fail to identify and characterize neutrophilic cells.

Human CD56+CD39+ dNK cells support fetal survival through controlling trophoblastic cell fate: immune mechanisms of recurrent early pregnancy loss.

Decidual natural killer (dNK) cells are the most abundant immune cells at the maternal-fetal interface during early pregnancy in both mice and humans, and emerging single-cell transcriptomic studies have uncovered various human dNK subsets that are disrupted in patients experiencing recurrent early pregnancy loss (RPL) at early gestational stage, suggesting a connection between abnormal proportions or characteristics of dNK subsets and RPL pathogenesis. However, the functional mechanisms underlying this association remain unclear. Here, we established a mouse model by adoptively transferring human dNK cells into pregnant NOG (NOD/Shi-scid/IL-2Rγnull) mice, where human dNK cells predominantly homed into the uteri of recipients. Using this model, we observed a strong correlation between the properties of human dNK cells and pregnancy outcome. The transfer of dNK cells from RPL patients (dNK-RPL) remarkably worsened early pregnancy loss and impaired placental trophoblast cell differentiation in the recipients. These adverse effects were effectively reversed by transferring CD56+CD39+ dNK cells. Mechanistic studies revealed that CD56+CD39+ dNK subset facilitates early differentiation of mouse trophoblast stem cells (mTSCs) towards both invasive and syncytial pathways through secreting macrophage colony-stimulating factor (M-CSF). Administration of recombinant M-CSF to NOG mice transferred with dNK-RPL efficiently rescued the exacerbated pregnancy outcomes and fetal/placental development. Collectively, this study established a novel humanized mouse model featuring functional human dNK cells homing into the uteri of recipients and uncovered the pivotal role of M-CSF in fetal-supporting function of CD56+CD39+ dNK cells during early pregnancy, highlighting that M-CSF may be a previously unappreciated therapeutic target for intervening RPL.

Abstract 314: Fixative eXchange (FX)-seq reveals single nucleus transcriptional landscapes of archived clinical cancer FFPE specimens

Abstract The wide availability of FFPE tissues stored in hospitals and biobanks could potentially be an invaluable resource to gain insights for improved medical treatment towards precision medicine by combining genomic and transcriptomic data with pathological annotations and medical records. Nevertheless, the majority of current single-cell sequencing methods depend on fresh or fresh-frozen samples to generate high-quality transcriptome data due to the limited reverse transcription (RT) yields in FFPE samples. Consequently, single-cell level transcriptomic studies of FFPE samples have been a longstanding challenge. Here, we present Fixative-eXchange (FX)-seq, a highly scalable snRNA-seq method for heavily paraformaldehyde (PFA)-fixed and/or FFPE samples. We successfully analyzed a total of nearly 320k nuclei from various samples, including PFA-fixed tissue, FFPE blocks, FFPE and hematoxylin and eosin (H&E)-stained sections from mouse brain and human clinical cancer specimens. In a human gastrointestinal stromal tumor (GIST) FFPE sample, we observed rare transcriptomic transitions toward increased epigenetic modifications affected by acquired resistance in response to prolonged imatinib treatment. Furthermore, in an archived human colorectal cancer (CRC) FFPE sample, spatially-contextualized FX-seq with pathologist annotation distinguishes tumor cells from epithelial progenitors along with identification of the tumor microenvironment (TME) such as cancer-associated fibroblasts (CAFs). We then proceeded to investigate transcriptional programs associated with drug resistance and lack of response in immunotherapy observed in CRC patients. With a hypothesis-driven curation, we compared transcription profiles at the single-nucleus level observed in MSS and MSI-H CRC patients from archived FFPE blocks and found distinctive transcriptional profiles from each group. Overall, FX-seq holds promise for clinical research, precision medicine, biomarker discovery, and molecular diagnosis using archived clinical FFPE specimens. Citation Format: Han-Eol Park, Yu Tak Lee, Jaewon Lee, Junsuk Lee, Hyelin Ji, Young-Lan Song, Yoon Dae Han, Hyunki Kim, Chang Ho Sohn. Fixative eXchange (FX)-seq reveals single nucleus transcriptional landscapes of archived clinical cancer FFPE specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 314.