Clinical challenges of tissue preparation for spatial transcriptome.

Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCN) trained with a masked-central-spot prediction objective. Trained on 416 human Visium samples spanning 15 organs, SAGE-FM learns spatially coherent embeddings that recover masked genes robustly, with 91% of masked genes showing significant correlations (p < 0.05). The SAGE-FM generated embeddings outperform MOFA and spatial transcriptomics in unsupervised clustering and preservation of biological heterogeneity. SAGE-FM generalizes to downstream tasks, enabling 81% accuracy in pathologist-defined spot annotation in oropharyngeal squamous cell carcinoma and improving glioblastoma subtype prediction relative to MOFA. In silico perturbation experiments further show that the model captures directional ligand-receptor and upstream-downstream regulatory effects consistent with ground truth. These results demonstrate that simple, parameter-efficient GCNs can serve as biologically interpretable and spatially aware foundation models for large-scale spatial transcriptomics.

Effects of storage temperature, storage time, and hemolysis on the RNA quality of blood specimens: A systematic quantitative assessment

Seed genebanks must maintain collections of healthy seeds and regenerate accessions before seed viability declines. Seed shelf life is often characterized at the species level; however, large, unexplained variation among genetic lines within a species can and does occur. This variation contributes to unreliable predictions of seed quality decline with storage time. To assess variation of seed longevity and aid in timing regeneration, ten varieties of pea (Pisum sativum L.), chickpea (Cicer arietinum L.) and lentil (Lens culinaris Medikus subsp. culinaris) from the Australian Grains Genebank were stored at moderate temperature (20°C) and moisture (7–11% water, relative humidity [RH] ~30%) and deterioration was assessed by yearly germination tests for 20 years. Decline in germination was fit to a sigmoidal model and the time corresponding to 50% germination (P50) was used to express seed longevity for each genetic line. The feasibility of using RNA fragmentation to assess changed seed health was measured using RNA integrity number (RIN) from RNA extracted from seeds that were stored for 13 and 20 years. Seed lots of legume grains that maintained high survival throughout the 20 years (i.e. they aged slower than other lines) had higher RIN than samples that degraded faster. RIN was lower in embryonic axes compared with cotyledons in the more deteriorated samples, perhaps indicating that axes exhibit symptoms of ageing sooner than cotyledons. Overall, RIN appears to be associated with longevity indicators of germination for these legumes and indicating that RIN decline can be used to assess ageing rate, which is needed to optimize viability monitoring.

IntroductionBio-repositories are invaluable resources to implement translational cancer research and clinical programs. They represent one of the most powerful tools for biomolecular studies of clinically annotated cohorts, but high quality samples are required to generate reliable molecular readouts and functional studies. The objective of our study was to define the impact of cancer tissue ischemia time on RNA and DNA quality, and for the generation of Patient-Derived Xenografts (PDXs).MethodsOne-hundred thirty-five lung cancer specimens were selected among our Institutional BioBank samples. Associations between different warm (surgical) and cold (ex-vivo) ischemia time ranges and RNA quality or PDXs engraftment rates were assessed. RNA quality was determined by RNA integrity number (RINs) values. Fresh viable tissue fragments were implanted subcutaneously in NSG mice and serially transplanted.ResultsRNAs with a RIN>7 were detected in 51% of the sample (70/135), with values of RIN significantly lower (OR 0.08, P = 0.01) in samples preserved for more than 3 hours before cryopreservation. Higher quality DNA samples had a concomitant high RIN. Sixty-three primary tumors (41 adenocarcinoma) were implanted with an overall engraftment rate of 33%. Both prolonged warm (>2 hours) and ex-vivo ischemia time (>10 hours) were associated to a lower engraftment rate (OR 0.09 P = 0.01 and OR 0.04 P = 0.008, respectively).ConclusionRNA quality and PDXs engraftment rate were adversely affected by prolonged ischemia times. Proper tissue collection and processing reduce failure rate. Overall, NSCLC BioBanking represents an innovative modality, which can be successfully executed in routine clinical settings, when stringent Standard Operating Procedures are adopted.

In spaceflight experiments, model organisms are used to assess the effects of microgravity on specific biological systems. In many cases, only one biological system is of interest to the Principal Investigator. To maximize the scientific return of experiments, the remaining spaceflight tissue is categorized, documented, and stored in the biobank at NASA Ames Research Center, which is maintained by the Ames Life Science Data Archive (ALSDA). The purpose of this study is to evaluate the state of a sample set of tissues from the ALSDA biobank. Garnering information – such as downstream functional analysis for the generation of omics datasets – from tissues is, in part, dependent on the state of sample preservation. RNA integrity number (RIN) values have been calculated for rodent liver tissues that were part of scientific payloads returned from the International Space Station (ISS). Rat livers from Spacelab Life Sciences 1 (SLS-1) and mouse livers from Commercial Biomedical Test Module 3 (CBTM-3), Rodent Research 1 (RR1), and Rodent Research 3 (RR3) were tested. It was found that mean RIN values from CBTM-3, RR1, and RR3 were suitable for downstream functional analysis (RIN &gt; 5) while the mean RIN value for SLS-1 was not (RIN = 2.5 ± 0.1). Information from this study lays the foundation for future efforts in determining the types of assays that are most appropriate for different tissues in the ALSDA biobank and similar preservation facilities, which would aid in shaping the design of experiments.

BackgroundThe integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way.MethodsA method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability.ResultsThis approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN).ConclusionOur results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.

The effects of preanalytical variables in tissue processing and storage periods on RNA quality of tissues have been well documented in each type of cancer. However, few studies have been performed on a comparative assessment of the impacts across different cancer tissues, even though it is well known that RNase activity is highly variable in various tissue types and RNase-rich tissues have been found to yield low-quality RNA. We investigated the impacts of cold ischemia times and long-term storage on RNA integrity in various types of cancer tissue, which had been fresh-frozen and collected at the Samsung Medical Center Biobank. RNA quality was also evaluated with regard to histopathological variables. We analyzed RNA integrity number (RIN) data, which had been obtained from our quality control (QC) processes over the last 7 years. Approximately 2% of samples were randomly selected and processed to measure RIN quarterly and after 6 years of storage for QC purposes. Fresh-frozen tumor tissues yielded high-quality RNA regardless of tumor type and histopathological features. Up to 1-hour cold ischemia times and up to 6-year storage times did not adversely influence RNA integrity. Only 3 samples showed RIN of <7 out of a total of 396 analyzed tumor tissues. Tissue quality was not adversely affected by long-term storage or limited variations of cold ischemia times. The low-quality samples could be correlated with the structural composition or intratumoral heterogeneity of tissues. The strict application of standardized protocols for tissue collection is the key for high-quality biobanking.

Oxford Nanopore direct RNA sequencing (DRS) is capable of sequencing complete RNA molecules and accurately measuring gene and isoform expression. However, as DRS is designed to profile intact RNA, expression quantification may be more heavily dependent upon RNA integrity than alternative RNA sequencing methodologies. It is currently unclear how RNA degradation impacts DRS or whether it can be corrected for. To assess the impact of RNA integrity on DRS, we performed a degradation time series using SH-SY5Y neuroblastoma cells. Our results demonstrate that degradation is a significant and pervasive factor that can bias DRS measurements, including a reduction in library complexity resulting in an overrepresentation of short genes and isoforms. Degradation also biases differential expression analyses; however, we find that explicit correction can almost fully recover meaningful biological signal. In addition, DRS provided less biased profiling of partially degraded samples than Nanopore PCR-cDNA sequencing. Overall, we find that samples with RNA integrity number (RIN) >9.5 can be treated as undegraded and samples with RIN >7 can be utilized for DRS with appropriate correction. These results establish the suitability of DRS for a wide range of samples, including partially degraded in vivo clinical and post-mortem samples, while limiting the confounding effect of degradation on expression quantification.

Spatial transcriptomics is a rapidly emerging field that allows for the study of gene expression within a tissue sample. This technology has the potential to provide valuable insights into the molecular mechanisms underlying cancer progression and could be particularly useful in identifying specific regional cancer progression. By studying the regional spatial trajectory of cancer progression, we can develop more targeted therapies and better predict the likelihood of metastasis. Additionally, analyzing gene expression patterns in specific regions of the tissue can reveal the molecular mechanisms driving cancer growth in those areas, potentially uncovering new therapeutic targets. In this study, we created a spatial trajectory analysis method, named as stLearn - PSeudo-Time-Space (PSTS) algorithm, to investigate specific regional cancer progression. Spatial trajectory analysis is a statistical method that allows for the identification of patterns of transitional gene expression over time and space within a tissue sample. In detail, this method incorporates spatial features, such as tissue sub-structure locations and gene expression changes, to model the trajectory from a single snapshot of transcriptomics data. Also, we evaluated the effectiveness of PSTS by utilizing the cancer progression model in human breast cancer samples. Using this approach, we were able to identify specific regions within the breast tissue that displayed distinct progression branching trajectories patterns of transitional gene expression associated with ductal carcinoma in situ and invasive ductal carcinoma. These regions were found to be characterized by a high level of cellular diversity and were associated with aggressive forms of breast cancer. The results of this study suggest that spatial transcriptomics and spatial trajectory analysis could be a powerful tool for identifying specific regional cancer progression and may help to inform the biomarkers for the development of targeted therapies for breast cancer. Overall, this study highlights the potential of spatial transcriptomics and spatial trajectory analysis to provide valuable insights into the molecular mechanisms underlying cancer progression and could have significant implications for the diagnosis and treatment of cancer. Citation Format: Duy Pham, Quan Nguyen. A novel spatial trajectory inference method for detecting regional breast cancer progression from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB076.

Introduction: Liver metastasis (LM) occurs in approximately 13% of patients with lung cancer at the time of diagnosis and is associated with poor prognosis. Currently, there are no effective preventive or therapeutic strategies specifically targeting LM. Investigating the LM microenvironment can not only elucidate the mechanisms underlying its development but also provide opportunities to identify novel therapeutic targets. Methods: An LM-prone mouse model was established by repeatedly injecting and harvesting Lewis Lung Carcinoma (LLC) cells from LM lesions. The spatial transcriptomics (Visium, 10X Genomics) was applied to explore the microenvironment and intratumoral heterogeneity of LM. Validation was conducted using single-cell RNA sequencing and spatial proteomics (PhenoCycler System, Akoya Bioscience) on human and murine LM samples, respectively. Bioinformatics analyses, including Gene Set Enrichment Analysis (GSEA), were applied to infer gene function and associated behaviors. Gene set variation analysis (GSVA) was performed to evaluate lung cancer patient survival in relation to the expression of the BZW2-associated gene set. Functional validation of BZW2 was conducted in vitro by assessing cell migration using transwell and wound healing assays, and by examining epithelial-to-mesenchymal transition (EMT) markers via immunoblotting. Results: Bzw2 is the significantly upregulated differential expressed gene in LM, particularly at the invasive front, based on the murine spatial transcriptomic data. The finding was further validated in murine LM samples using spatial proteomics as well. In human single-cell RNA sequencing data, BZW2 exhibited high expression in LM subclusters characterized by aggressive phenotypes, including enhanced proliferation, hypoxia, mesenchymal traits, invasiveness, and cancer stemness. GSVA analysis demonstrated that elevated expression of the BZW2-associated gene set was associated with poorer patient survival and enrichment of EMT-related pathways using the TCGA LUAD dataset. Furthermore, in vitro functional assays showed that BZW2 inhibition suppressed cell migration and reduced the expression of EMT markers, as confirmed by immunoblotting. Conclusion: By integrating findings from spatial transcriptomics, proteomics, and single-cell RNA sequencing, this study elucidates the role of BZW2 in promoting the aggressiveness of LM in lung cancer through its influence on cell migration and metastasis. These results suggest that BZW2 could serve as a promising therapeutic target worthy of further advanced research for LM in lung cancer. Citation Format: Kuan-Li Wu, Ying-Ming Tsai, Ya-Ling Hsu. BZW2: a novel therapeutic target for liver metastasis in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5299.

ABSTRACTAimTo analyse gene expression profiles in human tissue samples obtained from dental implant sites with or without peri‐implantitis.Materials and MethodsSoft‐tissue biopsy samples obtained from patients presenting with implants with severe peri‐implantitis and adjacent reference implants with no signs of peri‐implantitis were analysed by spatial transcriptomics (n = 4), RNA‐sequencing (n = 20) and immunohistochemistry (n = 38).ResultsThe analysis disclosed evident associations between distinct gene clusters and specific tissue compartments in both groups of specimens. Samples from peri‐implantitis sites showed higher levels of gene activity than samples from reference implant sites. Gene‐set enrichment analysis revealed that several important biological pathways, for example, ‘regulation of antimicrobial host response’, ‘collagen fibril organisation’ and ‘regulation of angiogenesis’, were severely dysregulated in peri‐implantitis lesions when compared to reference implant sites. Similarly, immunohistochemical evaluation showed elevated levels of proteins corresponding to the most up‐regulated, differentially expressed genes (DEGs), which were identified by spatial transcriptomics and RNA‐sequencing.ConclusionsPeri‐implant lesions demonstrated associations between gene clusters and specific tissue compartments. Several biological pathways linked to the activation of host response towards bacterial insults were dysregulated in peri‐implantitis specimens when compared to reference implant samples.

BackgroundSynucleinopathies and Alzheimer's disease (AD) share both common and distinct molecular and pathological features, but a comprehensive characterization of these diseases remains elusive.MethodsWe constructed a single‐nucleus and spatial transcriptome atlas of synucleinopathies and AD, analyzing 3.4 million nuclei from 513 post‐mortem human brain samples. Using 167 transcriptomic meta‐programs, we elucidated the complex molecular mechanisms underlying both diseases. We also examined co‐dysregulated meta‐programs across different cell types in each patient.ResultsNotably, we identified 44 disease‐specific meta‐programs, including inflammatory responses in microglia, across synucleinopathies and AD. We further revealed co‐dysregulation of microglia and oligodendrocyte precursor cells (OPCs) in AD. In contrast, synucleinopathies demonstrated co‐dysregulation of astrocytes, microglia, and vascular cells. These co‐dysregulated transcriptomic meta‐programs were verified by spatial transcriptomics using MERFISH.ConclusionOur integrative analysis of single‐cell and spatial transcriptomes provides insights into the shared and unique molecular signatures underlying neurodegenerative diseases.

Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial EnSemble Domain detection method that integrates results from different spatial domain detection methods to improve spatial domain detection. SpatialESD captures both direct cooccurrence patterns and multiscale indirect relationships between clusters, improving the robustness and accuracy of spatial domain detection. We evaluated SpatialESD on simulated datasets and multiple 10x Visium spatial transcriptomics datasets, including human brain, breast cancer, and ovarian cancer samples. The results show that SpatialESD consistently outperforms individual methods and the existing EnSDD ensemble method in terms of clustering accuracy and stability. Based on the identified domains, we further detected domain-specific differentially expressed genes and performed trajectory and cell-cell interaction analyses. These results reveal spatial patterns of gene expression and cellular communication, offering insights into tissue organization and disease mechanisms. Overall, SpatialESD provides a reliable and effective solution for spatial domain detection in ST data and facilitates downstream biological discovery.

Recent advances in spatial transcriptomics (ST) and spatial proteomics (SP) technologies have enabled high-dimensional molecular profiling at single-cell resolution, providing deeper insights into the tumour-immune microenvironment. However, these modalities are typically applied to separate tissue sections, limiting direct comparisons across molecular layers. We developed a wet-lab and computational framework to perform and integrate ST and SP from the same tissue section, as demonstrated on human lung cancer samples. Applying ST, SP, and hematoxylin and eosin (H&E) staining from the same section ensured consistency in tissue morphology and spatial context. Computational registration using Weave software allowed accurate alignment and annotation transfer across modalities. This co-registered dataset enabled single-cell level comparisons of RNA and protein expression, revealed segmentation accuracy and transcript-protein correlation analyses within individual cells. Notably, we observed systematic low correlations between transcript and protein levels—consistent with prior findings—now resolved at cellular resolution. Our approach highlights the feasibility and utility of performing spatially-resolved multi-omics analysis on the same section without compromising data quality, facilitating concordance studies and region-specific analysis of immune and tumour markers, and ultimately advancing our understanding of disease heterogeneity at the molecular level.

BackgroundGene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures.ResultsWe found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF.ConclusionWe have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.