Science AI Journal
Biochemistry, Genetics and Molecular Biology · Research topic

Open research questions in Single-cell and spatial transcriptomics

185 unresolved questions extracted from the limitations and future-work sections of 876 Single-cell and spatial transcriptomics papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Recurrence of adamantinomatous craniopharyngioma (ACP) is relatively common in clinical practice, yet the mechanisms underlying its recurrence remain poorly understood.

    Elucidating the recurrence mechanisms of adamantinomatous craniopharyngioma · 2026 · DOI

  • The dynamic evolution of Luminal subsets and their crosstalk with the tumor microenvironment (TME) during malignant progression remain poorly understood.

    High-Resolution Spatial Transcriptomics Reveals Pathological Microregion-Specific Luminal Subset Plasticity and Core Signaling Networks Underlying Prostate Cancer Malignancy · 2026 · DOI

  • Abstract Despite growing evidence on the impact of intra-tumoral heterogeneity (ITH) in breast cancer (BC), its biological drivers and clinically relevant mitigation strategies remain poorly characterized, particularly in low-resource settings where spatial profiling is rarely applied.

    Spatially dissecting drivers of inter- and intra-tumoral heterogeneity in Kenyan breast cancer · 2026 · DOI

  • Increasing evidence suggests that stromal cell heterogeneity critically shapes vascular remodeling; however, the specific fibroblast states associated with AAA rupture predisposition remain poorly defined.

    COL10A1+ fibroblasts define rupture-prone abdominal aortic aneurysms and enable molecular risk stratification · 2026 · DOI

  • Abstract Introduction: TROP2 (TACSTD2) is a validated therapeutic target in non-small cell lung cancer (NSCLC), yet mechanisms governing its interaction with the tumor immune microenvironment (TIME) and optimal combination strategies remain incompletely defined.

    Abstract A031: Large language models ensemble deciphers spatial proteogenomic landscapes to identify a novel trop2-cd47 co-targeting axis in non-small cell lung cancer · 2026 · DOI

  • Second, and importantly, the present in vitro experiments do not yet constitute conclusive experimental validation of the expression trends: because of the inconsistency between the Sections 2, 3 described above, the current qPCR and Western blot data cannot be regarded as fully reliable, and the expression changes of the four hub genes therefore remain to be confirmed by rigorously designed and internally consistent experiments.

    Identification of common diagnostic biomarkers and immune landscapes in sepsis and acute kidney injury: a transcriptomic study integrating machine learning and single-cell analysis · 2026 · DOI

  • Our platform provides a generalizable strategy to advance precision oncology, improve diagnostic accuracy, and facilitate equitable access to data-driven care for patients with rare and understudied malignancies.

    Abstract A026: AI-Enabled Digital Pathology and Multi-Omics Integration for Cell-Type–Resolved Biomarker Discovery in Environment-Associated Cancers · 2026 · DOI

  • C_LIO_LIIslet capillaries are closely associated with endocrine cells and are surrounded by specialized BMs; however, the cellular sources of these BM components in the adult human pancreas remain incompletely defined.

    The Spatial Landscape of Extracellular Matrix Gene Expression in Healthy and Type 2 Diabetic Human Pancreas · 2026 · DOI

  • While animal models imply that endothelial cells (ECs) are the exclusive source of islet BM, the precise cellular origins and spatial organization of the human islet matrisome remain poorly defined due to overlap in genes that mark non-epithelial cell populations and loss of spatial context during single-cell dissociation.

    The Spatial Landscape of Extracellular Matrix Gene Expression in Healthy and Type 2 Diabetic Human Pancreas · 2026 · DOI

  • Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimers disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear.

    The SEA-AD DREAM Challenge: Community benchmarking human and AI agent solutions for Alzheimer's disease neuropathology prediction from single-nucleus transcriptomics · 2026 · DOI

  • Thus, the present study evaluates this established TD-based con- struction in the CITE-seq setting, while robustness under alternative preprocessing schemes remains to be examined.

    Interpretable Integration of CITE-seq RNA and ADT Profiles Without Explicit Modality-Weight Tuning via Tensor Decomposition-Based Unsupervised Feature Extraction · 2026 · DOI

  • Notably, we 566 further demonstrate SpatialFuser’s unique capability for cross-resolution integration of 567 weakly correlated modalities, which has not been previously established.

    SpatialFuser: a unified framework for integrative analysis of unpaired spatial multi-omics data · 2026 · DOI

  • While pre-trained foundation models are now widely available for single-cell RNA-seq, comparable resources for bulk RNA-seq remain scarce, motivating a model that learns a unified, tissue-aware representation directly from bulk data.

    An atlas-scale generative model for unified representation learning of bulk RNA-seq data · 2026 · DOI

  • A major limitation of existing single-cell large language models (scLLMs) is that they rely on numeric expression data with gene names as the only textual signal, while comprehensive biomedical priors -- cellular localization, gene function, disease associations, and signaling interaction patterns -- remain absent from the model input.

    CellTosg2Sequence: A Unified Text-Omics-Signaling-Graph Large Language Model for Single-Cell Analysis · 2026 · DOI

  • Importantly, while the assay is designed for biomarker discovery and exploratory immune monitoring in clinical trials, its clinical utility, therapeutic‐response performance, and feasibility in multi‐site real‐world trial logistics remain to be established in ongoing studies involving specific disease cohorts.

    A Modular High‐Parameter Flow Cytometry Framework: Pre‐Analytical Optimization and Validation for Clinical Research · 2026 · DOI

  • Background Type 1 diabetes (T1D) is an autoimmune disease characterized by progressive β-cell destruction, yet current risk stratification tools, which rely mainly on genetic susceptibility and autoantibody profiles, remain insufficient for accurately predicting disease progression.

    From islet to blood: macrophage remodeling signatures for diagnosis and risk stratification in type 1 diabetes · 2026 · DOI

  • The technological evolution of spatial transcriptomics represents a persistent trade-off among resolution, throughput, and sensitivity, which is fundamentally anchored in the advancement of spatial barcoding chemistry. By transitioning from the precision of photochemical labeling and microfluidic parallelism in in situ coding strategies to the iteration of microarrays, bead arrays, and photolithographic grids in in situ capture strategies, physical limits have been pushed from multicellular resolution to submicron and even nanometer scales, thereby enabling the mapping of subcellular architectures. However, a critical bottleneck remains in balancing subcellular resolution, genome-wide coverage, and an ultra-large field of view, as these three parameters are not simultaneously satisfied by any current platform. Because static spatial atlases are no longer sufficient to decipher the intricate dynamics of development and disease, a paradigm shift from static snapshot-based spatiotemporal transcriptomics to metabolic RNA labeling-based spatiotemporal transcriptomics is necessitated. By fusing metabolic labeling with spatial capture arrays, RNA synthesis and degradation rates can be directly quantified within the native tissue context, effectively establishing true spatiotemporal resolution for the first time. Although current spatiotemporal transcriptomics technologies persistently encounter challenges, future technological iterations are anticipated to advance along three core trajectories: balancing high resolution with high sensitivity, integrating multi-omics dimensions, and enhancing clinical accessibility. Key breakthrough areas encompass optimizing spatial resolution and transcript capture efficiency at subcellular or single-molecule levels, enabling the in-situ co-detection of multi-omics modalities, adapting single-molecule long-read sequencing for spatial applications, and improving compatibility with clinical archival samples, such as FFPE tissues. Concurrently, the universality and accessibility of these platforms will be further elevated by streamlining technical workflows and controlling costs. At the frontier of technological evolution, the in-situ integration of in vivo metabolic RNA labeling with high-resolution spatial transcriptomics is regarded as one of the most groundbreaking future directions. By preserving the high-throughput and high-resolution advantages of existing spatial transcriptomics while introducing the temporal dimension of transcriptional dynamics, a technological leap from three-dimensional spatial localization to four-dimensional spatiotemporal dynamics is facilitated by this pathway. Current metabolic labeling predominantly relies on short-read sequencing, resulting in the loss of splice isoform information. However, by incorporating long-read technologies, the production rates of specific isoforms can be observed within four-dimensional space. For instance, in mammalian neural development and plasticity research, the in situ tracking of the minute-scale synthesis and turnover of synapse-associated splice variants is enabled, providing novel tools to decipher 20 Life Anal.

    Next-generation sequencing-based spatiotemporal transcriptomics: the next wave of spatial transcriptomics · 2026 · DOI

  • In medical contexts, it is insufficient for a controller to perform well empirically if its actuation logic is biologically opaque. Instead of asking whether a bacterium can express a therapeutic protein, the framework asks when expression should occur, what evidence should justify it, and how the system should behave when evidence is equivocal.

    Quantum-AI Control and Network Design of Engineered Living Therapeutics for Precision Immunomodulation in Chronic Inflammatory Disease · 2026 · DOI

  • Background: The DExH-box helicase 9 (DHX9)/interleukin enhancer-binding factor 3 (ILF3) complex participates in RNA processing and post-transcriptional regulation, but its behavior during lung adenocarcinoma cell differentiation remains poorly defined, particularly at single-cell resolution.

    Dynamic Regulatory Mechanisms of the DHX9/ILF3 Complex During Lung Adenocarcinoma Cell Differentiation: A Single-Cell Transcriptomic Analysis · 2026 · DOI

  • Abstract Liver hepatocellular carcinoma (LIHC) is a heterogeneous malignancy with poor prognosis, but the landscape of cellular senescence-related genes (CSRGs) in LIHC remains incompletely characterized.

    Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma · 2026 · DOI

  • M2 macrophages, hereafter referred to as MAC-M2, have been implicated in renal fibrosis, yet whether M2 macrophages are pro- or anti-fibrotic remains controversial, and the spatial context in which MAC-M2-fibrosis coupling occurs is unknown.

    Applying Spatial Statistics to Spatial Transcriptomics Reveals Local Association Between M2-like Macrophages and Fibrosis in Diabetic Kidney Disease · 2026 · DOI

  • Conclusions This database provides an interactive resource to access spatial gene expression, substructures, and regulatory networks across 50 developing human organs, supporting further research into the mechanisms of human organogenesis.

    HESTA: a curated and reusable database for the human early organogenesis spatiotemporal transcriptome atlas · 2026 · DOI

  • However, current assays remain limited to RNA readouts, lacking information on other phenotypic and mechanistic layers such as chromatin accessibility, protein abundance and post-translational modifications.

    Massively multiplex multimodal chemical screens at single-cell resolution · 2026 · DOI

  • Nucleoli, nuclear speckles and other compartments regulate transcription, RNA processing, and chromatin organization within the nucleus, yet the relationship of their morphology to developmental gene expression programs in vivo is poorly understood.

    Embryo-scale Visual Cell Sorting reveals a conserved transcriptomic signature of nucleolar size linked to proteostasis · 2026 · DOI

  • Most of the human genome is transcribed into diverse isoforms whose tissue specificity is profoundly disrupted in cancer, yet isoform-level dysregulation remains poorly characterized across solid tumors.

    Solid Tumors Pan Cancer Transcriptome: Tissue/Cancer specific expression groups at the Isoform-Level · 2026 · DOI

Most-cited papers in Single-cell and spatial transcriptomics

Most recent work

Find a gap in your own Single-cell and spatial transcriptomics sub-topic

This page shows what the Single-cell and spatial transcriptomics literature already flags as unresolved. To narrow it to your specific question, run the guided finder — it searches the gap library on demand and checks candidates against 250M+ OpenAlex works.

Open the Research Gap Finder →

Related topics in Biochemistry, Genetics and Molecular Biology

Gut microbiota and health
289 open questions
dental development and anomalies
121 open questions
Mitochondrial Function and Pathology
110 open questions
Genetic Associations and Epidemiology
106 open questions
Human-Animal Interaction Studies
105 open questions
Genomics and Chromatin Dynamics
97 open questions
Inflammatory Bowel Disease
91 open questions
Extracellular vesicles in disease
91 open questions
185 open questions have been extracted from the limitations and future-work passages of 876 Single-cell and spatial transcriptomics papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 120,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? A Tier 1–5 acceptance probability in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck prior-publication & salami-slicing risk across 8 sources.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.