Abstract Background The precise molecular delineation of the interface between the Subiculum (Sub) and cornu ammonis 1 (CA1) is a challenge in hippocampal research, as conventional cytoarchitectural boundaries are often ambiguous and limit reproducible regional annotation. Here, we developed a molecularly anchored spatial transcriptomic framework to define CA1–Sub regional identities using high-definition spatial transcriptomics (Stereo-seq) and single-nucleus RNA sequencing (snRNA-seq) references. Findings Using a human hippocampal Stereo-seq dataset from 12 donors, we established a data-driven parcellation framework that defines reproducible molecular features distinguishing CA1 and Sub while capturing the transition between these regions. FN1 was identified as a Sub-enriched marker in a subset of EX_Sub and, together with ETV1 and additional regional markers, enabled molecular assignment of CA1 and Sub identities across datasets. The Sub association of FN1 and ETV1 was further supported by human 10X Genomics spatial transcriptomics, mouse in situ hybridization data, and a mouse spatial transcriptomic dataset. Applying this framework to Alzheimer’s disease tissues revealed region-specific transcriptional alterations across CA1 and Sub, including enrichment of mitochondrial energy metabolism-related transcripts in the Sub, suggesting exploratory transcriptional associations of altered metabolic function. Conclusions This study provides a molecularly anchored framework for human CA1–Sub parcellation that complements conventional annotation. By defining regional molecular states while preserving the biological continuum across CA1–Sub interface, this approach enables more consistent regional analysis of human hippocampus tissue across donors, datasets, and disease conditions.
Accurate nuclear segmentation and classification (NuSC) in immunohistochemistry (IHC)-stained whole-slide images is essential for reliable biomarker quantification in computational pathology. Although existing NuSC methods perform well on Hematoxylin and Eosin images, they frequently underperform on IHC-stained images owing to stain heterogeneity, low nuclear contrast, and scarce biomarker-specific annotations. Prior efforts based on stain normalization or cross-domain knowledge transfer have yielded only marginal improvements under these conditions. To address these limitations, we introduce FPNuNet, a frequency-aware prompt-guided network tailored for NuSC of IHC-stained images. FPNuNet extracts multi-source features through four parallel encoders: a frozen SAM-based structural encoder and a frozen UNI-based semantic encoder-both adapted via lightweight discrete cosine transform-based prompt generators-together with a wavelet feature encoder and a multi-scale context encoder that capture complementary spatial and frequency-domain descriptors from the raw input. A discrete cosine transform-enabled frequency-aware fusion neck integrates all four feature streams with spectral enhancement, and three collaborative decoder branches jointly predict binary masks, horizontal-vertical vectors, and nuclear types. FPNuNet is evaluated on CD47-IHCNuSC, a dataset comprising 86 CD47-stained esophageal cancer patches with 18,483 manually annotated nuclei spanning seven clinically meaningful subtypes. On CD47-IHCNuSC, FPNuNet achieves the best instance-segmentation and aggregate subtype-classification performance among all evaluated methods under challenging IHC staining conditions.
Tea (Camellia sinensis L.), a major global economic crop in Asia, poses challenges for genetic identification because its highly heterozygous, repetitive genome reduces the efficacy of conventional single-nucleotide polymorphism (SNP) and microsatellite markers, and interspecific hybridization further complicates the situation. To address these issues, CamK-DB was developed as a reference-free Camellia fingerprinting database built on MIKE MinHash sketches. We curated 418 candidate resequencing datasets, and built a database using standardized 5× genome-coverage fingerprints. Each accession is stored as a MIKE. jac fingerprint generated with k = 21 and recommended sketch/pre_cnt = 2000. CamK-DB provides a command-line interface for data management and a custom C++ query engine that computes top-10 matches using Jaccard similarity, complemented by a QT-based graphical interface for interactive analysis. This resource offers a robust and scalable framework for precise and routine germplasm identification, genomic phylogenetic inference, and strategic breeding program design. CamK-DB (database and code) is publicly available at https://github.com/sc-zhang/CamK-DB. CamK-DB binaries are provided for Windows 10/11 and Linux (x86_64, glibc ≥ 2.27).
Single-cell long-read transcriptomics (scLR-seq) extends single-cell analysis beyond gene abundance by resolving full-length transcript structures in individual cells. It can directly interrogate isoform usage, alternative splicing, and transcription start and end site selection, thereby revealing regulatory variation that is often obscured by short-read measurements. In this review, we examine the experimental and computational foundations of scLR-seq, including platform selection, library design, cell barcode and unique molecular identifier recovery, transcript discovery, and isoform quantification. We discuss how these choices influence the reliability of downstream biological interpretation, and summarize emerging insights into isoform usage, alternative splicing, transcription start and end site selection, allele-specific expression, fusion transcripts, transposable element-derived transcripts, and RNA modifications. Finally, we highlight applications of scLR-seq in diverse biological systems, such as the immune system, neural development, and tumor microenvironments, and consider future opportunities and challenges in integrating multi-omics data to decode cellular programs and disease evolution.
